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OpenAI Software Engineer Interview Guide 2026

This guide covers the OpenAI software engineer interview process in 2026, including application and resume review, recruiter screens, skills-based......

Topics: OpenAI, Software Engineer, interview guide, interview preparation, OpenAI interview

Author: PracHub

Published: 3/17/2026

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OpenAI Software Engineer Interview Guide 2026

This guide covers the OpenAI software engineer interview process in 2026, including application and resume review, recruiter screens, skills-based......

5 min readUpdated Jun 24, 2026140+ practice questions
140+
Practice Questions
3
Rounds
7
Categories
5 min
Read
Contents
TL;DRSample QuestionsAbout the Interview ProcessWhat to expectThe interview processApplication and resume reviewRecruiter or introductory screenSkills-based assessment / technical screenSystem designPast-project or systems presentationFinal coding roundsBehavioral, values, and mission alignmentTeam fit / hiring manager conversationsStage-by-stage summaryWhat they testWhat good looks like in each roundWorked examplesHow to stand outPractice nextFAQHow long is the OpenAI software engineer interview process?Does OpenAI ask LeetCode-style algorithm questions?Is the interview remote or onsite?How important is mission and safety alignment?What's different about system design at OpenAI versus other companies?How should I prepare if I'm targeting an applied or product-facing team?FAQ
Practice Questions
140+ OpenAI questions
OpenAI Software Engineer Interview Guide 2026

TL;DR

This guide is for software engineers preparing for an OpenAI interview loop in 2026 — across early-career, mid, senior, and applied/product-facing roles. It walks through each stage you may encounter, what each one actually tests, concrete examples of how to answer well, and the mistakes that quietly sink candidates. The goal is to replace vague "study harder" advice with a clear map of the process and what good looks like at each step. OpenAI's process is structured but team-dependent. A typical path runs from application review to an introductory call, one or more skills-based assessments, a final interview loop, and then a decision. The defining theme is practical engineering over puzzle-heavy interviewing: expect coding that resembles real work, system design grounded in production constraints, and repeated evaluation of how you handle ambiguity, safety, reliability, and user impact.

Interview Rounds
HR ScreenOnsiteTechnical Screen
Key Topics
System DesignCoding & AlgorithmsBehavioral & LeadershipSoftware Engineering FundamentalsML System Design
Practice Bank

140+ questions

Estimated Timeline

2–4 weeks

Browse all OpenAI questions

Sample Questions

140+ in practice bank
System Design
1.

Design a Distributed Rate Limiter

NoneSystem Design

Design a distributed rate limiting system for a large API platform.

The platform runs many API gateways and backend services across multiple regions. For every incoming request, the rate limiter must decide whether the request is allowed or rejected, based on configurable limits. Your design should support, at minimum:

  • Multi-dimensional limits — requests per second scoped per user, per API key, per tenant, per IP address, or per endpoint (and combinations thereof).
  • Tier-aware limits — different limits for different subscription tiers (e.g. free vs. pro vs. enterprise).
  • Burst handling — short spikes above the steady-state rate should be tolerated up to a defined cap.
  • Dynamic configuration — limits and rules can be updated without redeploying gateways or backend services.

Your design should address the request flow, the rate-limiter API, the data model, the rate-limiting algorithm, distributed coordination across gateways and regions, consistency trade-offs, failure handling, scalability, and observability.

"Rate limiter" spans several products. Start by separating **functional** requirements (the allow/reject decision, the multi-dimensional + tiered limits, burst, dynamic config) from **non-functional** ones (latency budget, $10^6$ req/s throughput, availability, multi-region). Also state what's explicitly **out of scope** — e.g. volumetric DDoS defense lives upstream at the edge, not here.

Do a back-of-envelope pass: how many *rules* (and therefore counter operations) does one request trigger if several dimensions apply at once? Multiply by $10^6$ req/s. The op count plus the few-ms budget should force three conclusions about whether each check can touch a remote service, whether it can scan, and whether counter state can live on one node.

Lay the standard families side by side — fixed window, sliding-window log, sliding-window counter, leaky/token bucket — and grade each on burst tolerance, accuracy, and per-op cost. Watch for the fixed-window **boundary problem** (a window edge can admit up to 2× the limit). Ask which structure can express *both* a burst allowance and a sustained rate at once, and how a per-request weight/`cost` would ride on top for heterogeneous workloads. Reason about the properties; don't commit to one and design it out here.

The read-modify-write of a counter has to collapse into a **single atomic step** at the store, or two concurrent requests both read the old value and both pass. Think about what primitive your chosen store offers to make that one operation indivisible. Separately, ask whether you can derive elapsed-time effects (e.g. refills/decay) **on read** instead of running a background job per key.

A single node can't serve millions of checks/sec, so the counter state has to spread across many nodes. The constraint that shapes *how* you spread it: each atomic check must still land on one node. Ask what you'd partition on so a given counter always resolves to the same place, and what that choice does to a request that triggers several counters at once. Then stress it: a whale tenant or a shared API key concentrates traffic onto one partition — how do you relieve that, and could a gateway-side fast path keep most traffic off the store entirely?

A cross-region round trip per request is off the table, yet some limits are *global*. That tension forces a spectrum: at one end you enforce everything locally and reconcile global truth out-of-band; at the other you keep one authoritative counter everyone consults. Sketch where the endpoints land on latency, availability, and accuracy, pick a default, and say plainly which property you're trading away — a
Solution
2.

Design a sandboxed cloud IDE

EasySystem Design

System Design: Sandboxed Cloud IDE (Colab-like)

Design a multi-tenant, browser-based cloud IDE/notebook that lets users run code inside an isolated sandbox (similar to a hosted notebook product such as Colab). The defining challenge is safely executing arbitrary, untrusted user code at scale while keeping the experience interactive.

Core User Experience

  • A user opens a workspace (project/notebook), edits code in the browser, and runs cells or shell commands.
  • Output appears in the UI: stdout/stderr plus rich output (plots, tables, images).
  • Users can watch streaming logs while their code is still running.

Requirements

Functional

  • Provision an isolated compute environment per workspace/session.
  • Execute arbitrary user code safely (sandboxing).
  • Stream execution output and logs to the browser in near real time.
  • Support basic file operations (upload/download, persisted workspace state).
  • Basic real-time collaboration is optional — call it out explicitly if you choose to include it.

Non-functional

  • Strong isolation between tenants — security is the primary, non-negotiable constraint.
  • Reasonable startup latency when launching a new session.
  • Support autoscaling and fair resource sharing across users/orgs.
  • Observability: metrics, tracing, and audit logs.

Focus Areas to Cover

  1. Compute substrate — how you choose and manage it (VMs vs. containers vs. microVMs vs. userspace-kernel sandboxes), including the GPU case.
  2. Isolation model — filesystem, network, process/kernel, and credentials.
  3. Log/output streaming architecture — near-real-time, resumable, backpressure-aware.
  4. Lifecycle management — create, run, idle, suspend/resume, terminate.
  5. Data persistence strategy — workspace files, runtime overlay, checkpoints/snapshots.

Please state your assumptions, provide an API sketch, and describe a high-level architecture (a diagram described in text is fine).

The single hardest requirement here is **running untrusted code**. Treat the user as an adversary trying to escape the sandbox, steal credentials, or reach internal services — then optimize latency, density, and cost *subject to* that isolation guarantee, not the other way around.

A common clean structure for this problem is a **multi-tenant control plane** (stateless: auth, scheduling, lifecycle, quotas) versus a **per-session data plane** (the actual sandboxes). Ask yourself which components must hold per-session state — that tier won't be stateless.

The answers to "concurrent active vs. idle tail" and "cold-start SLA" are what justify two of the biggest design levers — **warm pools** (for fast start) and **suspend-to-snapshot** (so the idle tail costs near-zero). Anchor those numbers early.

Size the fleet from **both** vCPU and RAM and take the binding dimension. Then notice that the idle/suspended tail must cost ~0 in running CPU/RAM — that single observation drives the suspend/snapshot strategy more than any other number.

For untrusted code, the **shared host kernel** is the attack surface: one kernel CVE = full host compromise across tenants. Rank options by *what kernel they share* — plain containers share the host kernel; a userspace-kernel sandbox or a microVM does not.

Pick a default substrate from your ranking and justify it on the three-way tension between isolation strength, cold-start, and density. Then pressure-test it against the GPU case: does whatever you chose for the common workload actually let you attach an accelerator? If not, what has to be different for those sessions?

Outbound network is where exfiltration and credential theft happen. From inside the guest, what is the most dangerous thing reachable over the network in a typical cloud environment, and how do yo
Solution
Coding & Algorithms
3.

Implement in-memory DB querying

MediumCoding & AlgorithmsCoding
Question

Implement an in-memory database that supports: 1. Querying the whole table and returning only selected columns (projection). 2. Adding WHERE clause filtering with simple conditions like (column, operator, value). 3. Adding ORDER BY on one or more columns with ascending/descending control. 4. Explaining how you would design and build an index to accelerate such queries (no code required). Example public API: db = DB() db.insert("users", {"id": "1", "name": "Ada", "birthday": "1815-12-10"}) … db.query("users", ["id"], conditions=[("name", "=", "Charles")], order_by=(["birthday"], False)) # returns sorted projection

Solution
4.

Implement credit ledger with out-of-order timestamps

HardCoding & AlgorithmsCoding

Problem

You are implementing a GPU credit ledger that supports adding credits, charging credits, and querying balances. Requests can arrive in any timestamp order (timestamps are not monotonic).

Design a data structure/class that supports these operations:

  • addCredit(timestamp, amount)
    • Records that amount credits were added at time timestamp.
  • chargeCredit(timestamp, amount)
    • Records that amount credits were requested to be charged at time timestamp.
  • getBalance(timestamp) -> integer
    • Returns the effective balance at time timestamp, computed using all recorded requests whose timestamps are <= timestamp.

Rules for computing the effective balance

When computing the balance at time T, consider all recorded addCredit and chargeCredit events with timestamp <= T and process them in increasing timestamp order.

  • Start from balance 0.
  • For an addCredit, increase the balance.
  • For a chargeCredit:
    • If current balance is >= amount, deduct it (the charge succeeds).
    • Otherwise, do not deduct it (the charge is declined/ignored).

Tie-breaking (same timestamp)

If multiple events share the same timestamp, process them in this order:

  1. All addCredit events at that timestamp (in insertion order)
  2. All chargeCredit events at that timestamp (in insertion order)

Notes

  • Requests arrive out of order; you are allowed to cache/store all requests.
  • There is no strict time complexity requirement; correctness is the priority.

Deliverable

Provide the API and implement the logic so that repeated calls to getBalance(T) always return the correct value according to the rules above.

Solution
Machine Learning
5.

Implement and Debug Backprop in NumPy

MediumMachine Learning

Two-Layer Neural Network: Backpropagation and Gradient Check (NumPy)

You are implementing a fully connected two-layer neural network for multi-class classification with the architecture:

Affine (XW1 + b1) → ReLU → Affine (·W2 + b2) → softmax → cross-entropy loss

Assume a mini-batch of N examples, input dimension D, hidden size H, and C classes.

TensorShapeMeaning
XN × Dmini-batch of inputs
y(N,)integer class labels in {0, 1, …, C−1}
W1, b1D × H, (H,)first affine layer
W2, b2H × C, (C,)second affine layer

This is a coding-and-debugging interview: it is as much about converting conceptual understanding into correct code and debugging skills as it is about the math. Across the parts below you will (1) derive the backprop equations, (2) implement the forward/loss/backward in NumPy, (3) build a gradient checker, and (4) walk through diagnosing a deliberate mismatch.

Constraints & Assumptions

  • Language/libraries: Python 3.7+, NumPy only. No automatic differentiation (no PyTorch/TensorFlow/JAX/autograd).
  • Vectorization: No explicit Python loops over the batch in the forward/backward path. (Loops are permitted inside the gradient checker.)
  • Loss: Mean (not sum) cross-entropy over the mini-batch, i.e. averaged by $1/N$.
  • Numerical stability: Softmax must subtract the row-wise max from the logits before exponentiating.
  • Scale of the toy problem: Small enough to grad-check by brute force (e.g. $N \le 10$, with $D, H, C$ in the single/low-double digits) so finite differences run quickly.
  • Precision: float64 throughout; finite-difference step $\varepsilon \approx 10^{-5}$, central differences.

Clarifying Questions to Ask

  • Is the loss the mean over the batch or the sum? (This fixes whether a $1/N$ factor appears in the gradients.)
  • Should I include an L2 regularization term on the weights, or just the data loss?
  • Do you want the gradient w.r.t. the input X as well, or only the parameter gradients (W1, b1, W2, b2)?
  • What is the ReLU convention at exactly $z = 0$ — subgradient 0 or 1? (Does it matter for grad-checking?)
  • What tolerance counts as "passing" the gradient check, and on what dataset size should I demonstrate it?
  • Can I assume labels y are integer class indices (not one-hot)?

Part 1 — Derive the backpropagation equations

Derive $\partial L/\partial W_1$, $\partial L/\partial b_1$, $\partial L/\partial W_2$, $\partial L/\partial b_2$.

  • Apply the chain rule explicitly, layer by layer.
  • State the tensor shape at each step.
  • Explain the linear-algebra identities you use — e.g. (AB)^T = B^T A^T, the broadcasting rule for the biases, and how the affine gradient ∂L/∂W = X^T · upstream arises.
Differentiate **back to front**: start from the loss, get $\partial L/\partial \text{scores}$, then push through the second affine, the ReLU, and the first affine in turn. Keep a running "upstream gradient" tensor at each stage.

The fused **softmax + cross-entropy** gradient is far simpler than its two pieces suggest — work out $\partial L/\partial \text{scores}$ for a single example and you'll find it collapses into the predicted probabilities and the true label, with no $C\times C$ Jacobian to build. Once you have the per-example form, don't forget the batch-averaging factor.

For an affine $Y = AB$, each parameter gradient is the upstream $G$ matrix-multiplied by *one* of the factors, transposed. You don't have to memorize which side or whether to transpose: only one arrangement yields a result whose shape matches the parameter, so derive the rule once and let `grad.shape == param.shape` choose it. A bias broadcast across every row is a separate case — think about how many terms it appears in.

Solution
6.

Debug a failing ML classifier

HardMachine Learning

Debugging a Churn Prediction Pipeline With Poor Generalization

Context

You have inherited a binary churn prediction system. The goal is to predict whether a customer will churn in the next period, using only information available up to an "as-of" cutoff time. The current numbers are:

  • Training ROC AUC: 0.95
  • Validation ROC AUC (random split): 0.62
  • Time-based holdout ROC AUC (most recent month): 0.55
  • Predicted probabilities are overconfident (scores cluster near 0 and 1, but observed outcomes do not match).
  • Positive-class prevalence ≈ 1:10 (about 9–10% positive).

Task

Describe, step by step, how you would debug this system. Your answer should cover:

  1. Data validation and leakage checks (including temporal leakage).
  2. Label and feature drift analysis.
  3. Cross-validation scheme selection.
  4. Error analysis — by slices, calibration, and threshold-dependent confusion matrices.
  5. Ablations and feature audits.
  6. Training issues — regularization, class weighting, resampling.

Propose concrete experiments to isolate the root causes, name the metrics you would inspect, and recommend fixes plus a plan to verify improvements and prevent regressions (tests, data versioning, monitoring).

There are *two* separate gaps, not one. Train→random-val and random-val→time-holdout each isolate a different failure. Ask what each split holds out — **rows** vs **time** vs **entities** — and what a gap at each stage implies.

At 1:10 prevalence, ROC AUC is the wrong headline number, and **overconfidence is a calibration problem that AUC cannot see at all**. Decide up front which symptoms are *ranking* failures and which are *calibration* failures — they have different diagnostics and different fixes.

Leakage can enter at more than one stage of the data and split pipeline — consider the timeline of *when* data was available, the identity of *who* appears in each fold, and *how* preprocessing transforms were fit relative to the split boundary. Each entry point produces a different pattern of inflated scores.

Some near-free experiments — ones that perturb the labels or isolate individual features — can distinguish leakage from genuine overfit in a single afternoon, long before you touch model architecture or hyperparameters.

Constraints & Assumptions

  • Features must use only events strictly before as_of_date; the label is derived strictly after it, over a fixed horizon $[t, t+H)$.
  • The model is trained offline and scored in batch; a deployed prior (base rate) may differ from the training prior, especially after any resampling/weighting.
  • Customers can have multiple rows over time (one per (customer_id, as_of_date)), so naive row-level random splits can place the same customer on both sides.
  • A retention action is taken at a chosen operating point (e.g. "contact the top-k highest-risk customers"), so decision quality at a threshold matters, not just global ranking.
  • Assume you can re-run the training pipeline, re-cut splits, and add tests, but you cannot collect new ground-truth faster than labels mature.

Clarifying Questions to Ask

  • What is the exact churn label definition and horizon $H$ — voluntary vs involuntary, paid-activity-based vs login-based, and how are late-within-horizon churners and right-censored (not-yet-matured) recent customers handled?
  • How are features built relative to the cutoff — fixed lookback windows? Any absolute dates or fields (contract-end, cancellation flags) that could be set by or after the churn event?
  • How were the current splits constructed — random by row or grouped by customer? Were preprocessing transforms and any target encoding fit before or after the split?
  • What is the **business operating point and cos
Solution
ML System Design
7.

Design a Text-to-Video Generation System

HardML System Design

Design a Sora-like text-to-video generation platform.

Users submit a text prompt, optional generation settings (duration, resolution, fps, seed, model variant), and possibly optional conditioning media such as an init image or reference clip. The system generates a short video and returns a downloadable result once the job is complete. Because a single clip takes seconds-to-minutes of GPU time, the system is inherently asynchronous: a submit returns a job handle immediately, and the user polls or receives a webhook when the result is ready.

Your design should cover the user-facing API and job lifecycle, the high-level service architecture, how GPU inference workers are scheduled, how the system handles unstable workers / crashes / retries / partial failures, how intermediate and final artifacts are stored, how safety / rate limits / quotas are enforced, and how quality / latency / reliability are monitored. The hardest, most-probed areas are (a) the job lifecycle / state machine and (b) failure handling when workers are unstable — go deep on both.

Anchor the whole design on the **inference profile**: GPU-bound, long-running, expensive, failure-prone. That single observation forces an **async, queue-backed** architecture where the database is the source of truth for job state and the GPU is the scarce resource you schedule for fairness and utilization, not request throughput.

Draw an explicit **state machine** with terminal states (e.g. `COMPLETE`, `FAILED_PERMANENT`, `CANCELED`, `TIMED_OUT`) that never transition out, and give every `RUNNING` job a deadline so a wedged worker can't hang forever. Make `POST /videos` **idempotent** via an `Idempotency-Key` so a client retry on timeout never double-creates (and double-bills) a GPU job.

Pick a single mechanism that lets the database decide who owns a job and when it has died, and that survives a worker that disappears and later comes back. Think about what damage a partitioned worker that reconnects could do to a job already retried elsewhere — and what property each result write would need to carry so that a stale, returning attempt can never win over the one the system already trusts.

Don't model "a job failed" as one thing. Sort failures by what the *right reaction* is — would retrying as-is have any chance of succeeding, would it only succeed under different conditions, or is it guaranteed to fail again? Map each class to retry / retry-differently / give-up, and ask what intermediate state a long job could persist so that a recovered attempt resumes cheaply instead of restarting from zero.

The GPU is what you're optimizing for, so reason about what makes it idle or unfair: cold model-weight loads, one user monopolizing the pool, and small kernels. Think about which jobs you'd want to land on the *same* worker (weight affinity), how you'd stop a flood of free-tier work from starving paid traffic (priority + fair queuing), and the latency cost you pay when you micro-batch jobs together to fill the GPU.

Constraints & Assumptions

State your own numbers in the interview — the figures below are illustrative, not benchmarks.

  • A single job is a short clip (e.g. 5–10 s at up to 720p, 24 fps), taking on the order of tens of seconds to a few minutes of GPU wall-time on one accelerator — 3–6 orders of magnitude slower than a typical web request.
  • The scarce, expensive resource is the GPU accelerator. API, queue, and database compute are cheap by comparison; the design optimizes for GPU utilization and fairness, not request throughput.
  • Jobs are long-running and stateful mid-flight. Worker crashes, OOMs, and spot-instance preemptions are routine, not exceptional — fault tolerance is a first-class requirement.
  • Output is regulated content: both the prompt and the generated frames must pass saf
Solution
8.

Design a GPU credit system and scheduler

HardML System Design

Design a GPU Credit Accounting and Scheduling Service (Technical Screen)

You are designing a backend service for an ML platform that runs training and inference jobs on heterogeneous GPUs (e.g., A100, H100). Users and teams purchase credits and consume them while their jobs run. Design the system end to end: the credit ledger, the reservation/metering flow, and the scheduler that places jobs on GPUs.

The system is multi-tenant, multi-project, and multi-region, and must:

  • Prevent double-spend under concurrency, retries, and races.
  • Schedule fairly across users and teams.
  • Handle preemption and failures with correct partial refunds.

Constraints & Assumptions

Anchor the design to these. Where a number is not given, state the assumption you make and design to it.

  • GPU pricing is per GPU-hour and differs by GPU type.
  • Jobs specify resource requirements: GPU-type preferences (ordered), GPU count, and a memory floor.
  • Jobs may be preempted according to policy; some jobs are non-preemptible.
  • Suggested sizing to design against (adjust and justify if you prefer different numbers): tens of thousands of accounts, low-thousands of concurrently running jobs, and per-job metering heartbeats on the order of every 30–60 s. The metering write path is therefore the highest-QPS mutation, while reservation and settlement are lower QPS but must be strictly correct.

Functional Requirements

1. Credit lifecycle

  • Issuance (purchases, grants, promotions) and expiration.
  • Balance queries with a breakdown (promotional vs. paid, upcoming expirations).
  • Spend ordering across credit buckets (e.g., earliest-expiring first).

2. Reservation and metering

  • Idempotent reservation at job submission that checks budgets and quotas.
  • Metered consumption while a job runs: commit actual usage, and partially refund the unused hold on completion, preemption, or failure.

3. Budgets and quotas

  • Per-user and per-project budgets, with hierarchical limits (team/org → project → user).
  • Promotional credits with separate policies and expiration.

4. Scheduling

  • Place jobs on heterogeneous GPUs based on their requirements and available quota/credits.
  • Fairness across users/teams, with support for weights/priority classes and preemption.

5. Audit and observability

  • An immutable audit trail for all credit and scheduling decisions.
  • Metrics, logs, and traces for SLOs and debugging.

Non-Functional Requirements

  • APIs must be idempotent and concurrency-safe, with rate limits.
  • Protect against double-spend under races and retries.
  • State your consistency choices explicitly (strong vs. eventual) and handle clock skew.
  • Describe sharding/scaling strategies for high throughput.

Clarifying Questions to Ask

A strong candidate scopes the problem before designing. Good questions to raise with the interviewer:

  • What is the read:write split, and which path is hottest — balance reads, reservations, or metering heartbeats? (This decides what to optimize and where eventual consistency is acceptable.)
  • How strict are the hierarchical budgets — are org/project/user limits hard (reject on breach) or soft (alert only), and may a brief overshoot be tolerated for the largest orgs?
  • When a job is placed on a non-preferred GPU type (e.g., an H100-preferring job lands on an A100), which type's price applies, and is the price fixed at start or allowed to change mid-run?
  • On completion, preemption, or failure, where does the unused hold go — back to the exact buckets it was drawn from (preserving source and expiry), or into a fresh balance?
  • What is the preemption contract — is there a checkpoint grace period before a job is killed, and are non-preemptible jobs ever reclaimed for capacity (vs. only stopped when out of credits)?
  • Where does an account's money "live" relative to where its jobs run — sin
Solution
Behavioral & Leadership
9.

Explain your perspective on AI safety

HardBehavioral & Leadership

You are working in a company that builds and deploys advanced AI systems (e.g., large language models, recommendation systems, vision models) that are used by millions of users.

Question:

How do you think about AI safety in this context?

In your answer, discuss:

  • What "AI safety" means to you in practical, product-building terms.
  • The main categories of risks you are concerned about when deploying AI systems (for both near-term and longer-term horizons).
  • How you, in your role as an engineer or technical leader, would incorporate AI safety into the lifecycle of building, evaluating, and operating AI features.
  • Any concrete processes, tools, or examples (from past experience or hypothetical) that illustrate your approach.

Structure your response as if you were answering this in a behavioral interview, and be specific about how you balance innovation with responsible deployment.

Solution
10.

Answer project deep dive and cross-functional questions

EasyBehavioral & Leadership

Behavioral / leadership round prompts

You’re asked to cover some or all of the following:

  1. Technical deep dive presentation

    • Prepare a short slide deck explaining one of your projects.
    • Interviewer probes on depth: architecture, trade-offs, failures, what you would redo, and what you specifically owned.

  2. Motivation & mission

    • “Why do you want to work here (e.g., OpenAI)?”
    • “What is your view on AGI and its impact/risks?”

  3. Negative / conflict questions (examples)

    • Tell me about a time you made a mistake.
    • A time you disagreed with a teammate/leadership.
    • A time you received tough feedback or failed to deliver.

  4. Cross-functional (XFN) with a PM

    • Describe how you work with PMs.
    • How do you pitch an idea, align stakeholders, and handle pushback?

Provide structured, specific answers with clear outcomes and reflections.

Solution
Software Engineering Fundamentals
11.

Model particle hits on a screen

HardSoftware Engineering Fundamentals

A point source at (0, 0) emits particles toward an infinite vertical screen located at x = 1. For each particle, sample an angle theta uniformly from [-pi/2, pi/2] relative to the positive x-axis, and let the particle travel in a straight line until it hits the screen.

  1. Let Y be the y-coordinate where the particle hits the screen. Express Y in terms of theta.
  2. Derive the CDF and PDF of Y.
  3. Write a simulation that generates many particles and verifies the theoretical result by comparing a normalized histogram of the sampled hit locations with the true density.
  4. Briefly explain any numerical or visualization issues you would expect in this simulation.
Solution
12.

Design a social network with snapshots

MediumSoftware Engineering Fundamentals

You are asked to design and implement an in-memory SocialNetwork class that supports users following each other and creating snapshots of the follow graph. A snapshot is an immutable view of all follow relationships at the moment it was created; later mutations to the live network must not change any previously taken snapshot.

Implement an API similar to:

sn = SocialNetwork()
sn.add_user("A")
sn.add_user("B")
sn.follow("A", "B")

snap = sn.create_snapshot()
assert snap.is_follow("A", "B")

sn.follow("A", "C")          # mutates the live graph only
assert not snap.is_follow("A", "C")   # the old snapshot is unaffected

The follow relation is directed: follow(u, v) means u follows v and does not imply v follows u. Your task is to design the underlying data structures, choose a snapshot strategy, and implement the three operations below.

Constraints & Assumptions

  • In-memory, single-process; no persistence required.
  • User identifiers are hashable (treat them as strings/ints).
  • is_follow, listing followers/followees, and recommendations all operate on a given snapshot, not the live graph.
  • Snapshots may be taken frequently relative to the number of edges; reads on a snapshot should be cheap.
  • State the asymptotic time/space cost of each operation and of create_snapshot itself.

Clarifying Questions to Ask

  • If u or v does not exist when calling follow / is_follow / a listing, should the method throw, auto-create the user, or return a benign default (false / empty)?
  • Is self-follow (follow(u, u)) permitted, or rejected?
  • Are repeated follow(u, v) calls idempotent (no duplicate edge, no inflated counts)?
  • Roughly how many snapshots are expected versus how many users/edges — i.e. is the bottleneck snapshot creation, snapshot memory, or query latency?
  • Is there an unfollow operation, and must snapshots taken before/after it reflect the right state?

Part 1 — Snapshot follow query

Support snap.is_follow(u, v) returning whether user u follows user v as of that snapshot.

The query is membership in *u*'s set of followees. A `Map<User, Set<User>>` keyed by the follower gives $O(1)$ average lookup; decide what to return when `u` is absent from the snapshot.

What This Part Should Cover

  • The core adjacency representation chosen and why it makes is_follow average $O(1)$.
  • Clear, consistent behavior when u (or v) is not present in the snapshot.

Part 2 — List followers and followees

For a given snapshot and user x, return:

  • the followers of x (everyone who follows x), and
  • the followees of x (everyone x follows).
Returning followers from an out-edge map alone forces an $O(V+E)$ scan. Maintaining a **second** adjacency map of *incoming* edges (`in[v] = followers of v`) makes both listings $O(\text{degree})$, at the cost of updating two maps on every `follow`.

What This Part Should Cover

  • Recognizing that efficient follower listing requires either a reverse index or accepting a full-graph scan, and the time/space trade-off between them.
  • Returning copies or read-only views so callers cannot mutate the snapshot.

Part 3 — Recommend users to follow

Given a snapshot, a user u, and an integer k, recommend up to k users for u to follow using a friends-of-friends ranking:

  1. Take F, the set of users u already follows.
  2. For each f ∈ F, look at whom f follows.
  3. Count, across all of u's followees, how many times each candidate c appears.
  4. Return the top-k candidates by that count, excluding u itself and anyone already in F.
This is a 2-hop traversal: iterate `u`'s followees, then *their* followees, accumulating a frequency count in a hash map. Decide the exclusion set (`u`, plus everyone already in `F`) before counting.

You don't need to fully sort all `m` candi
Solution
Data Manipulation (SQL/Python)
13.

Parse and build binary data in Python

MediumData Manipulation (SQL/Python)

Using provided interfaces ByteReader(read(n), read_uint32_le, read_string) and ByteWriter(write(b), write_uint32_le, write_string), implement functions to pack and unpack messages for a simple binary protocol: message = {id:uint32 LE, payload_len:uint32 LE, payload:bytes}. Write parse_message(reader)->Message and build_message(writer, Message)->None with error handling for short reads, invalid lengths, and overflow. Avoid printing for debugging; design tests instead, and explain how you would verify correctness and performance.

Solution

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About the Interview Process

What to expect

This guide is for software engineers preparing for an OpenAI interview loop in 2026 — across early-career, mid, senior, and applied/product-facing roles. It walks through each stage you may encounter, what each one actually tests, concrete examples of how to answer well, and the mistakes that quietly sink candidates. The goal is to replace vague "study harder" advice with a clear map of the process and what good looks like at each step.

OpenAI's process is structured but team-dependent. A typical path runs from application review to an introductory call, one or more skills-based assessments, a final interview loop, and then a decision. The defining theme is practical engineering over puzzle-heavy interviewing: expect coding that resembles real work, system design grounded in production constraints, and repeated evaluation of how you handle ambiguity, safety, reliability, and user impact.

The final loop can cover a lot in a short span. OpenAI has described finals as roughly 4-6 hours with 4-6 interviewers over one or two days, virtual by default with an onsite option in San Francisco. Treat any specific number of rounds or duration as typical rather than guaranteed; the exact format, length, and timeline vary by team and scheduling.

Flowchart of the OpenAI software engineer interview process from application to offer

The interview process

The stages below are common, but not every candidate sees all of them, and some are combined. Use this as a map of what can appear, not a fixed sequence.

Application and resume review

An asynchronous screen of your resume for technical impact, evidence of ownership, fast learning in unfamiliar domains, and relevance to OpenAI's product, infrastructure, or research-adjacent engineering needs. There's no live questioning here, so your projects and the scope you owned need to read clearly on paper. Lead each bullet with the outcome and your specific contribution, not the team's, and quantify where you honestly can.

Recruiter or introductory screen

A conversation (commonly 30-45 minutes, sometimes up to an hour) with a recruiter or hiring manager covering your background, why OpenAI, why this role or team, and logistics like location, hybrid expectations, and compensation. They're gauging communication, motivation, and whether your reasons for joining are specific and thoughtful. A generic "I love AI" answer is a missed opportunity here.

Skills-based assessment / technical screen

A practical technical round that varies by team, often totaling around two hours. It may be pair coding, a live exercise, an online assessment, or some combination, and some teams front-load both coding and a lighter system-design discussion. OpenAI evaluates implementation skill, code quality, correctness, testing habits, performance reasoning, and how well you turn vague requirements into something usable.

System design

For many mid-level and senior SWE roles, a dedicated system design interview appears (commonly 45-60 minutes), sometimes before finals and sometimes within the loop. It's usually a collaborative architecture discussion where you define scope, APIs, data models, scaling plans, and trade-offs. Interviewers care about scale, reliability, maintainability, latency, cost, abuse prevention, and whether the design fits the product's actual use case.

Past-project or systems presentation

A walkthrough of a system or project you genuinely owned, often a 45-60 minute session. It often works like reverse system design: the interviewer probes architecture, incidents, trade-offs, metrics, and what you'd redesign today. The aim is to distinguish real ownership from surface familiarity and to see how you reason in high-impact, ambiguous environments.

Final coding rounds

One or more coding interviews (each commonly around 60 minutes) that can go beyond standard algorithms into debugging, refactoring, code review, or implementing infrastructure-adjacent components under realistic constraints. The bar is whether you can write clean, maintainable, production-quality code while collaborating and reasoning aloud.

Behavioral, values, and mission alignment

At least one conversational round (commonly 30-60 minutes) on how you work and why OpenAI specifically. Expect questions about ownership, incident handling, cross-functional collaboration, prioritizing safety or reliability, and your views on responsible AI deployment. Mission alignment isn't confined to one round, but this is where it's probed most directly.

Team fit / hiring manager conversations

Some loops include extra discussions with a potential manager, teammates, or adjacent stakeholders to assess team-specific fit, how you work with researchers or product partners, and whether you can operate at the boundary of research and production. For applied roles, product sense and user-facing judgment can matter as much as backend depth.

Stage-by-stage summary

StageTypical lengthPrimary signalHow to prepare
Resume reviewAsyncImpact, ownership, fast learningOutcome-first bullets; clear scope
Recruiter screen30-45 minCommunication, specific motivationA concrete "Why OpenAI / why this team"
Skills assessment~2 hrsCode quality, correctness, testingPair-code in a plain editor; write tests
System design45-60 minTrade-offs, scale, reliabilityPractice API + data-model + failure modes
Project deep-dive45-60 minReal ownership, judgmentOne project you can defend end-to-end
Final coding~60 min eachProduction-quality code, collaborationDebug/refactor practice, think aloud
Behavioral / values30-60 minOwnership, mission alignmentSTAR stories; a real safety trade-off

What they test

Coding. Be ready for implementation-heavy tasks using common data structures, object-oriented design, string and stateful-component logic, debugging, refactoring, testing, and complexity analysis. The key difference from a purely algorithmic process is that interviewers tend to value readable code and sensible trade-offs over the cleverest possible solution. You may be asked to improve existing code, handle edge cases, add retries and timeouts, or reason about concurrency rather than solve abstract puzzles.

Systems. Get comfortable with distributed-systems fundamentals, API and data-model design, caching, rate limiting, authentication, usage tracking, idempotency, fault tolerance, observability, and scalability under high traffic. For OpenAI specifically, system design can extend into model-serving and API-platform concerns: streaming responses, variable-latency inference, quota enforcement, batching, GPU-aware constraints, and cost-versus-latency trade-offs. Interviewers also test reasoning under ambiguity — whether you can clarify requirements, choose sensible service boundaries, define metrics, plan rollback paths, and design for abuse prevention and safe deployment rather than raw throughput alone.

Ownership and mission fit. Beyond technical skill, expect repeated evaluation of ownership, communication, and motivation. You'll need to show that you can move quickly in unfamiliar domains, work cross-functionally, and make principled decisions in small, high-talent teams. In project and behavioral rounds, expect probing on incidents, trade-offs, monitoring, reliability improvements, and moments when you prioritized safety, user trust, or long-term maintainability over short-term speed.

Diagram of three evaluation dimensions OpenAI tests: coding, systems, and ownership

What good looks like in each round

It helps to know not just the topics but the behaviors interviewers reward versus penalize. The patterns below show up across coding, system design, and behavioral rounds.

DimensionWhat strong candidates doCommon pitfall
RequirementsClarify scope and constraints before codingJump straight into a solution
Code qualityReadable names, small functions, error handlingClever one-liners, no edge cases
TestingName test cases, handle empty/invalid input"I'd add tests later" with no specifics
System designState assumptions, discuss trade-offs and failureList components with no reasoning
ReliabilityTalk retries, timeouts, idempotency, rollbackOptimize only for the happy path
CommunicationThink aloud, take hints gracefullyGo silent, get defensive about feedback
Mission fitSpecific, honest reasons tied to your workGeneric enthusiasm for AI

Worked examples

These are illustrative examples of how to approach common moments, not real interview questions or transcripts.

Example coding moment. Suppose you're asked to implement a rate limiter for an API endpoint. A strong approach: first clarify the requirements out loud — "Is this per-user or per-IP? What's the limit and window? Should it be a fixed window or sliding? Is this single-process or distributed?" Then start with a clear, correct version (for instance, a token-bucket or sliding-window counter), name the edge cases (clock skew, concurrent requests, the first request, exceeding the limit), and only then discuss how you'd make it distributed with a shared store. Narrating those trade-offs is often worth more than a perfectly optimized solution delivered in silence.

Example system-design moment. If asked to design an API that streams model responses, a strong opening is to scope it: expected request volume, latency targets, and what "streaming" means for the client. From there you'd cover the request lifecycle (auth, quota check, queueing, inference, streamed tokens), then reliability (what happens on a dropped connection, partial response, or an overloaded backend), then cost-versus-latency knobs like batching and timeouts. Stating assumptions and failure modes explicitly signals production maturity.

Example behavioral answer (STAR). For "Tell me about a time you prioritized reliability over shipping speed":

Situation: Our team was about to launch a feature on a tight deadline. Task: I owned the rollout. Action: During canary testing I noticed an elevated error rate under load that we couldn't fully explain, so I pushed to hold the launch a few days, added monitoring and a rollback path, and root-caused a retry storm. Result: We shipped slightly late but with no incident, and the monitoring caught two later regressions before users did.

Keep it specific, honest, and centered on your own decisions and trade-offs.

How to stand out

  • Prepare a specific, credible answer to "Why OpenAI?" that connects your background to safe and useful AI deployment, not just enthusiasm for the field.
  • Practice coding in a plain editor and focus on production-quality implementation: clear structure, test cases, edge handling, and maintainability.
  • Clarify ambiguous requirements early in every technical round instead of jumping straight to a solution. This is a strong signal here.
  • In system design, explicitly discuss latency, cost, rate limits, abuse prevention, observability, rollback plans, and failure modes, not just high-level boxes and arrows.
  • Pick one past project you understand end-to-end and rehearse a walkthrough covering architecture, incidents, metrics, trade-offs, and what you'd redesign now.
  • Surface examples where you protected reliability or safety even when it slowed shipping, because responsible deployment reads as a positive signal.
  • If you're targeting a senior or applied team, be ready to explain how you bridge research and production and collaborate with researchers, PMs, and other partners under ambiguous goals.

Practice next

The fastest way to close gaps is to practice on questions that mirror the real loop:

  • Browse real OpenAI interview questions reported by candidates, across coding, system design, and behavioral rounds.
  • Work through the broader software engineer interview track for role-level fundamentals.
  • Drill implementation and system-design problems in the full question bank.
  • Explore more company-specific walkthroughs in the interview guide library and other resources.

FAQ

How long is the OpenAI software engineer interview process?

It varies by team and scheduling, but a typical end-to-end timeline runs a few weeks from application to decision. The final loop itself is commonly described as roughly 4-6 hours, sometimes split across one or two days. Treat these as typical, not guaranteed.

Does OpenAI ask LeetCode-style algorithm questions?

Coding rounds use common data structures and complexity reasoning, but the emphasis leans toward practical, production-style tasks — implementation, debugging, refactoring, and edge cases — rather than obscure puzzles. Clean, readable, well-tested code is valued over the cleverest possible trick.

Is the interview remote or onsite?

OpenAI's interviews are virtual by default, with an onsite option in San Francisco for some candidates and teams. Logistics are usually confirmed during the recruiter screen.

How important is mission and safety alignment?

It matters and is probed directly, usually in a behavioral or values round. You don't need a manifesto, but you should be able to give specific, honest reasons OpenAI fits your goals and point to moments where you prioritized reliability, user trust, or safety over speed.

What's different about system design at OpenAI versus other companies?

The fundamentals are the same, but questions can extend into AI-platform concerns: streaming responses, variable-latency inference, quota enforcement, batching, GPU-aware constraints, and cost-versus-latency trade-offs. Reasoning about reliability and abuse prevention tends to count for a lot.

How should I prepare if I'm targeting an applied or product-facing team?

In addition to coding and systems, expect more weight on product sense, user-facing judgment, and how you collaborate with researchers and PMs under ambiguous goals. Be ready to discuss how you'd bridge research and production and make principled trade-offs when requirements aren't fully defined.

Frequently Asked Questions

Pretty hard. It felt less like a standard big-tech loop and more like a focused test of whether you can do real work with smart, fast-moving people. The official process varies by team, but OpenAI says engineering interviews look for well-designed solutions, high-quality code, performance, test coverage, communication, and collaboration. In practice, that means strong fundamentals are not enough by themselves. You need to code cleanly, explain tradeoffs well, and stay calm when the problem gets a little uncomfortable or open-ended.

From OpenAI’s interview guide, the flow starts with recruiter screening, then a skills-based assessment, then final interviews. The assessment can vary by team and may include pair coding, take-home work, or technical tests, and sometimes more than one assessment. Final interviews are usually 4 to 6 hours with 4 to 6 people over 1 to 2 days. My takeaway is: expect one early screen, one serious technical filter, then a longer onsite-style loop with coding, system thinking, and collaboration conversations.

If you already interview well for senior software roles, I’d give yourself about 3 to 6 weeks of focused prep. If you’re rusty, 6 to 10 weeks is more realistic. What helped me most was not endless LeetCode; it was practicing clean coding under pressure, talking through design choices, and doing realistic pair-programming sessions. OpenAI also recommends reading the OpenAI Charter, blog posts, and research that interests you. That matters because they want to see that you understand the company’s style, mission, and the kinds of problems teams are actually solving.

The big ones are coding quality, system design judgment, performance thinking, testing, and communication. OpenAI’s guide specifically says engineering interviews look for well-designed solutions, high-quality code, optimal performance, and good test coverage. I’d add comfort with ambiguity, because many OpenAI engineering roles sit close to research or fast-moving product work. Depending on the team, you may also need stronger depth in distributed systems, infrastructure, data pipelines, API design, or ML-adjacent tooling. The common thread is being able to build something practical, reliable, and easy for others to work with.

The biggest mistake is solving the problem in your head and not letting the interviewer into your thinking. A close second is writing code that technically works but is messy, untested, or hard to extend. I also think candidates get burned by treating it like a generic interview and not adapting to the team. OpenAI seems to care a lot about collaboration and real engineering judgment, not just speed. If you ignore tradeoffs, skip tests, freeze when requirements are vague, or show no interest in OpenAI’s mission and products, that hurts.

OpenAISoftware Engineerinterview guideinterview preparationOpenAI interview

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