As a Software Engineer at Character.AI, you are at the forefront of the consumer artificial intelligence revolution. Character.AI empowers over 20 million monthly users to connect, learn, and tell stories through interactive, personalized AI companions. In this role, you are not just maintaining legacy systems; you are building the core infrastructure, data flywheels, and safety alignments that define the next generation of human-computer interaction. The engineering culture here is incredibly fast-paced and high-impact. Because Character.AI operates at a massive scale—handling tens of millions of characters and infinite conversational permutations—Software Engineers must tackle unique challenges in distributed systems, data pipelines, and machine learning infrastructure. Whether you are on the AI Platform team optimizing distributed training on GPUs, or on the AI Safety & Alignment team mitigating model toxicity through Reinforcement Learning from Human Feedback (RLHF), your work directly shapes the product. You will collaborate closely with world-class ML researchers, product managers, and infrastructure engineers. Given the company's hyper-growth and recent unicorn status, the systems you build today will need to scale exponentially tomorrow. Expect to have a significant, visible impact on the product and the broader AI landscape within your very first weeks on the job.
Recruiter Phone Screen
reportedInitial call to align on your background, interests, and the specific engineering track that fits your profile.
What to demonstrate
- Initial call to align on your background, interests, and the specific engineering track that fits your profile
- Depth in Python
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Technical Phone Screen
reportedLive coding session focusing on data structures, algorithms, or practical data manipulation tasks.
What to demonstrate
- Live coding session focusing on data structures, algorithms, or practical data manipulation tasks
- Depth in Python
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Virtual Onsite Loop
reportedComprehensive stage consisting of four to five rounds, including coding interviews, system design, and behavioral interviews.
What to demonstrate
- Comprehensive stage consisting of four to five rounds
- Including coding interviews, system design, and behavioral interviews
How to prepare
- Answer aloud and timed: Design a distributed system to coordinate batch inference jobs across thousands of GPUs.
- Answer aloud and timed: Explain how you would implement RLHF from scratch. What are the most common pitfalls in the reward modeling phase?
PracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
Bias toward action and impact: During behavioral interviews, emphasize moments in your career where you identified a problem and independently drove the solution. Character.AI wants builders who do not need hand-holding.
Going into the loop without having done this.
Clarify constraints in System Design: Never start drawing boxes on a whiteboard without asking clarifying questions. At Character.AI's scale, knowing whether a system needs to handle 10,000 requests per second versus 1,000,000 requests per second entirely changes the architecture.
Going into the loop without having done this.
When discussing data pipelines, always highlight your understanding of data quality and observability. A pipeline that silently drops data is worse than a pipeline that crashes, especially when that data feeds into an RLHF training loop.
Going into the loop without having done this.
Brush up on Cloud Native concepts: Even if you are applying for an ML-heavy role, demonstrating a solid understanding of Docker, Kubernetes, and Terraform will set you apart. Infrastructure is everyone's responsibility in a fast-growing startup.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function to find the longest common substring among a massive batch of chat logs.
Write a function to find the longest common substring among a massive batch of chat logs.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Implement a thread-safe LRU cache to store recent conversation contexts for quick retrieval.
Implement a thread-safe LRU cache to store recent conversation contexts for quick retrieval.
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Given a stream of incoming chat messages, write an algorithm to maintain a sliding window of the top 10 most f
Given a stream of incoming chat messages, write an algorithm to maintain a sliding window of the top 10 most frequently used words.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Write a program to deserialize a custom binary format used for storing model weights into a usable Python obje
Write a program to deserialize a custom binary format used for storing model weights into a usable Python object.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Implement an algorithm to efficiently merge multiple sorted streams of log data based on timestamps.
Implement an algorithm to efficiently merge multiple sorted streams of log data based on timestamps.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
Write the update path that detects a concurrent edit
resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.
Approach
- One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
- Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
- State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
- Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.
Follow-up
- A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
- Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?
Design a real-time data pipeline to capture user feedback (thumbs up/down) on AI responses and feed it into a
Design a real-time data pipeline to capture user feedback (thumbs up/down) on AI responses and feed it into a training dataset.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How would you architect a rate-limiting service for our public API to prevent abuse while ensuring low latency
How would you architect a rate-limiting service for our public API to prevent abuse while ensuring low latency for legitimate users?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Walk me through the design of a system that serves personalized character recommendations to 20 million monthl
Walk me through the design of a system that serves personalized character recommendations to 20 million monthly active users.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Design a distributed system to coordinate batch inference jobs across thousands of GPUs.
Design a distributed system to coordinate batch inference jobs across thousands of GPUs.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Explain how you would implement RLHF from scratch. What are the most common pitfalls in the reward modeling ph
Explain how you would implement RLHF from scratch. What are the most common pitfalls in the reward modeling phase?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you quantitatively measure the "creativity" versus the "safety" of a generative language model?
How do you quantitatively measure the "creativity" versus the "safety" of a generative language model?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Describe a technique you would use to prevent a conversational AI from leaking personally identifiable informa
Describe a technique you would use to prevent a conversational AI from leaking personally identifiable information (PII) present in its training data.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you optimize PyTorch training loops to maximize GPU utilization when dealing with highly variable seque
How do you optimize PyTorch training loops to maximize GPU utilization when dealing with highly variable sequence lengths?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
What strategies would you use to debug a model that suddenly starts exhibiting toxic behavior after a recent f
What strategies would you use to debug a model that suddenly starts exhibiting toxic behavior after a recent fine-tuning run?
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
Built from the rounds and topics Character.AI candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Character.AI loop
- Write out the reported sequence: Recruiter Phone Screen, Technical Phone Screen, Virtual Onsite Loop.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.
02Work Python
- Spend the session on Python, which Character.AI candidates report being tested on.
- Write one worked example in Python and time yourself on it.
Deliverable: One timed worked example in Python.
03Work Data Pipelines
- Spend the session on Data Pipelines, which Character.AI candidates report being tested on.
- Write one worked example in Data Pipelines and time yourself on it.
Deliverable: One timed worked example in Data Pipelines.
04Work ML/LLMs
- Spend the session on ML/LLMs, which Character.AI candidates report being tested on.
- Write one worked example in ML/LLMs and time yourself on it.
Deliverable: One timed worked example in ML/LLMs.
05Answer out loud: Data and System Architecture
- Answer aloud, timed: Design a real-time data pipeline to capture user feedback (thumbs up/down) on AI responses and feed it into a training dataset.
- Answer aloud, timed: How would you architect a rate-limiting service for our public API to prevent abuse while ensuring low latency for legitimate users?
Deliverable: Spoken answers to 2 reported Data and System Architecture question(s), under time.
06Answer out loud: Machine Learning and AI Safety
- Answer aloud, timed: Explain how you would implement RLHF from scratch. What are the most common pitfalls in the reward modeling phase?
- Answer aloud, timed: How do you quantitatively measure the "creativity" versus the "safety" of a generative language model?
Deliverable: Spoken answers to 2 reported Machine Learning and AI Safety question(s), under time.
07Answer out loud: Coding and Algorithms
- Answer aloud, timed: Write a function to find the longest common substring among a massive batch of chat logs.
- Answer aloud, timed: Implement a thread-safe LRU cache to store recent conversation contexts for quick retrieval.
Deliverable: Spoken answers to 2 reported Coding and Algorithms question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
How do you handle schema evolution in a massive BigQuery data warehouse without disrupting downstream ML train
How do you handle schema evolution in a massive BigQuery data warehouse without disrupting downstream ML training jobs?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Estimate work you have never done and defend the range
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
Approach
- Decompose into independently deployable steps before estimating anything: add the column nullable, write both shapes, backfill in batches, verify, move reads, stop writing the old shape, drop it. That is four deploys spread over days, and the calendar estimate is dominated by them rather than by the loop's runtime.
- Do the arithmetic aloud for the part that has arithmetic in it: batch size times number of batches times per-batch duration, at a write rate the primary can absorb alongside roughly 1.2k writes per second of production traffic. The loop is throttled by replication lag and lock waits, not by how fast it can issue statements.
- Price the schema step by its lock rather than its statement duration. In PostgreSQL an ALTER TABLE taking ACCESS EXCLUSIVE waits for every open transaction on that table while later queries queue behind it, so a millisecond change issued during a thirty-second analytics query stalls that table for thirty seconds. Adding a nullable column with a non-volatile default avoids a rewrite from version 11; a new index wants CREATE INDEX CONCURRENTLY, which cannot run inside a transaction block and leaves an invalid index behind if it fails.
- Express the answer as a range whose endpoints each trace to a stated assumption, then name the cheapest experiment that collapses it, which is almost always running one real batch against the real table and multiplying.
Follow-up
- How do you verify the backfill genuinely finished, given rows written by production traffic while it ran?
- Where does the backfill resume from after a worker is killed mid-batch, and what makes that resume point trustworthy?
Tell callers you do not own that their integration breaks
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
Approach
- Establish the reader set empirically rather than from a wiki of owners: per-field usage counters keyed by principal, or access logs attributed to a consumer. State the blind spot of whichever you pick, since a consumer that reads the field only on a monthly job will not appear in a week of logs.
- Ship additive first. Populate the new field alongside the old one so no reader is forced to move, which is also what keeps a rolling deploy safe, because old and new instances answer the same requests at the same time and a rollback must still find the old shape present.
- Set the window from the slowest legitimate consumer's release cadence, not from your calendar, and decide separately what to do for a consumer with no release process at all, such as an external webhook endpoint you can only email.
- Convert silence into evidence before you rely on it: a short, low-traffic removal window that makes a still-dependent consumer fail visibly and loudly while you are watching, rather than at three in the morning after you have moved on.
Follow-up
- How would you detect a consumer that reads the field only during a monthly export?
- One caller refuses to move and has a commercial relationship behind it. What changes in your plan and what does not?
- 01
How do you handle schema evolution in a massive BigQuery data warehouse without disrupting downstream ML training jobs?
- 02
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
- 03
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
How much prior Machine Learning experience is required for the Software Engineer role?
It depends heavily on the track. For AI Platform and Data Engineering roles, deep ML expertise is not strictly required, though you must understand how ML models consume data and how to build infrastructure to support them (e.g., PyTorch familiarity). For Research Engineering and Safety roles, deep ML expertise, specifically with transformers and RLHF, is an absolute requirement.
Character.AI Software Engineer candidate reports ↗What is the company culture like at Character.AI?
The culture is highly autonomous, fast-paced, and impact-driven. Because the company is experiencing hyper-growth, there is a strong emphasis on a "get things done" mindset. Engineers are expected to be proactive, identify bottlenecks, and ship solutions without waiting for top-down direction.
Character.AI Software Engineer candidate reports ↗How should I prioritize my preparation time?
Focus first on ensuring your core coding and algorithm skills are flawless, as you cannot pass the technical screen without them. Next, dedicate significant time to System Design or Data Architecture, specifically focusing on cloud environments (GCP) and massive scale. Finally, review your domain-specific knowledge (Spark/Beam for Data, RLHF/Evaluation for Safety).
Character.AI Software Engineer candidate reports ↗How long does the interview process typically take?
The end-to-end process usually takes between two to four weeks, depending on interviewer availability and how quickly you complete the initial technical screens. The recruiting team is generally highly responsive and moves quickly for strong candidates.
Character.AI Software Engineer candidate reports ↗Are these roles remote or in-office?
These specific Software Engineering roles are based in Redwood City, CA. Character.AI places a strong emphasis on in-person collaboration, especially given the tight feedback loops required between engineering and research teams.
Character.AI Software Engineer candidate reports ↗How hard is the Character.AI interview?
Candidates most commonly rate Character.AI interviews as medium, based on 10 reported interviews.
Character.AI Software Engineer candidate reports ↗What topics does Character.AI test in interviews?
Character.AI interviews most often cover Python, SQL, Transformers, RLHF (Reinforcement Learning from Human Feedback), and Golang (Go). The exact emphasis depends on the specific role you apply for.
Character.AI Software Engineer candidate reports ↗Where is Character.AI headquartered?
Character.AI is headquartered in Palo Alto, US.
Character.AI Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01Character.AI Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
candidate · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
platform · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
platform · Accessed 2026-09-22
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