As a Software Engineer at Nanonets, you will play a critical role in building and scaling the next generation of Intelligent Document Processing (IDP) technology. Nanonets leverages advanced machine learning and deep learning models to help businesses automate document-heavy workflows, turning unstructured data into structured, actionable insights. In this role, you are not just writing standard application code; you are building the highly scalable, low-latency infrastructure required to ingest, process, and extract data from millions of complex documents daily. The engineering team at Nanonets tackles challenges at the intersection of system design, API performance, and machine learning deployment. Your work will directly impact the performance of core products, influencing how quickly and accurately global enterprises can process invoices, identity documents, and financial statements. Because the platform must handle heavy traffic spikes, complex asynchronous processing, and deep learning model inference in real time, your engineering decisions will directly dictate the reliability and speed of the entire system. This position is ideal for engineers who thrive in fast-paced, high-ownership environments. You will be expected to move quickly, take complete ownership of your services, and design systems that are both highly performant and resilient.
HR Introductory Call
reportedInitial call to discuss your professional background, career interests, and alignment with the role.
What to demonstrate
- Initial call to discuss your professional background, career interests, and alignment with the role
- Depth in Live coding
How to prepare
- Answer aloud and timed: Design the internal logic and class structure for an ATM machine, handling state transitions and cash dispensing limits.
- Answer aloud and timed: Implement a thread-safe in-memory cache with an LRU eviction policy, explaining how you would handle concurrent read and write operations.
Technical Evaluation Phase
reportedCore technical rounds that test your coding, system design, and project-handling capabilities.
What to demonstrate
- Core technical rounds that test your coding, system design, and project-handling capabilities
- Depth in Live coding
How to prepare
- Answer aloud and timed: Design a parking lot system, detailing the class structure, vehicle types, and payment calculation modules.
- Answer aloud and timed: Create a modular parser for document metadata that can easily be extended to support new document types and formats.
Executive and Cultural Alignment
reportedDeep-dive discussions with the CTO or CEO focusing on architectural philosophy and past engineering achievements.
What to demonstrate
- Deep-dive discussions with the CTO or CEO focusing on architectural philosophy and past engineering achievements
- Depth in Live coding
How to prepare
- Answer aloud and timed: Design a system where your core application service depends on two downstream services that support different rate limits. How do you scale this system to serve both paid and unpaid users?
- Answer aloud and timed: How would you design a priority scheduling mechanism for API requests to ensure that synchronous user requests are prioritized over asynchronous background processing?
PracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
To maximize your chances of success during the Nanonets interview process, keep these practical tips in mind:
Going into the loop without having done this.
Focus on working code: In live coding rounds, prioritizing a working, functional solution is critical. Do not get bogged down trying to write a perfect, highly optimized algorithm if it means you run out of time to actually run and test your code.
Going into the loop without having done this.
Clarify requirements early: In both LLD and HLD rounds, start by asking clarifying questions. Understand the scale, constraints, user tiers, and expected traffic patterns before you begin designing or writing any code.
Going into the loop without having done this.
Showcase real-world experience: During project deep-dives, don't just explain what your system did. Focus on why you built it that way, the specific technical bottlenecks you encountered, the trade-offs you evaluated, and how you measured success.
Going into the loop without having done this.
Be ready for API interactions: Make sure you are completely comfortable with making HTTP requests, parsing JSON, handling rate-limiting headers, and managing asynchronous execution in your programming language of choice.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Implement a thread-safe in-memory cache with an LRU eviction policy, explaining how you would handle concurren
Implement a thread-safe in-memory cache with an LRU eviction policy, explaining how you would handle concurrent read and write operations.
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 document processing logs, write an efficient algorithm to identify the top bottleneck stages
Given a stream of document processing logs, write an efficient algorithm to identify the top bottleneck stages in the pipeline.
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 custom priority queue that supports dynamic weight adjustments based on user tiers and request typ
Implement a custom priority queue that supports dynamic weight adjustments based on user tiers and request types.
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?
Solve a classic graph traversal or interval-merging problem, optimizing for both time and space complexity.
Solve a classic graph traversal or interval-merging problem, optimizing for both time and space complexity.
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?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
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 the internal logic and class structure for an ATM machine, handling state transitions and cash dispensi
Design the internal logic and class structure for an ATM machine, handling state transitions and cash dispensing limits.
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 parking lot system, detailing the class structure, vehicle types, and payment calculation modules.
Design a parking lot system, detailing the class structure, vehicle types, and payment calculation modules.
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?
Create a modular parser for document metadata that can easily be extended to support new document types and fo
Create a modular parser for document metadata that can easily be extended to support new document types and formats.
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 system where your core application service depends on two downstream services that support different
Design a system where your core application service depends on two downstream services that support different rate limits. How do you scale this system to serve both paid and unpaid 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?
How would you design a priority scheduling mechanism for API requests to ensure that synchronous user requests
How would you design a priority scheduling mechanism for API requests to ensure that synchronous user requests are prioritized over asynchronous background processing?
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 real-time document ingestion pipeline that can handle millions of uploads per day, ensuring data cons
Design a real-time document ingestion pipeline that can handle millions of uploads per day, ensuring data consistency and fault tolerance.
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 a distributed rate limiter across a cluster of API servers to prevent system a
Explain how you would implement a distributed rate limiter across a cluster of API servers to prevent system abuse while maintaining low latency.
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Write a functional Python script during a live coding session to integrate with a mock API, process the JSON p
Write a functional Python script during a live coding session to integrate with a mock API, process the JSON payload, and handle rate-limiting headers dynamically.
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Walk me through the architecture of the most complex system you have designed and deployed to production. What
Walk me through the architecture of the most complex system you have designed and deployed to production. What were the key technical trade-offs you made?
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 do you handle situations where you need to integrate with a poorly documented or unstable third-party API?
How do you handle situations where you need to integrate with a poorly documented or unstable third-party API?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
p99 jumped on one listing filter while p50 stayed flat
After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.
Approach
- Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
- Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
- Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
- Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.
Follow-up
- The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
- How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?
Built from the rounds and topics Nanonets candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Nanonets loop
- Write out the reported sequence: HR Introductory Call, Technical Evaluation Phase, Executive and Cultural Alignment.
- 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 Live coding
- Spend the session on Live coding, which Nanonets candidates report being tested on.
- Write one worked example in Live coding and time yourself on it.
Deliverable: One timed worked example in Live coding.
03Work System Design
- Spend the session on System Design, which Nanonets candidates report being tested on.
- Write one worked example in System Design and time yourself on it.
Deliverable: One timed worked example in System Design.
04Work Low-Level Design (LLD)
- Spend the session on Low-Level Design (LLD), which Nanonets candidates report being tested on.
- Write one worked example in Low-Level Design (LLD) and time yourself on it.
Deliverable: One timed worked example in Low-Level Design (LLD).
05Answer out loud: Low-Level Design (LLD)
- Answer aloud, timed: Design the internal logic and class structure for an ATM machine, handling state transitions and cash dispensing limits.
- Answer aloud, timed: Implement a thread-safe in-memory cache with an LRU eviction policy, explaining how you would handle concurrent read and write operations.
Deliverable: Spoken answers to 2 reported Low-Level Design (LLD) question(s), under time.
06Answer out loud: High-Level Design (HLD) & System Architecture
- Answer aloud, timed: Design a system where your core application service depends on two downstream services that support different rate limits. How do you scale this system to serve both paid and unpaid users?
- Answer aloud, timed: How would you design a priority scheduling mechanism for API requests to ensure that synchronous user requests are prioritized over asynchronous background processing?
Deliverable: Spoken answers to 2 reported High-Level Design (HLD) & System Architecture question(s), under time.
07Answer out loud: Data Structures, Algorithms & Live Coding
- Answer aloud, timed: Write a functional Python script during a live coding session to integrate with a mock API, process the JSON payload, and handle rate-limiting headers dynamically.
- Answer aloud, timed: Given a stream of document processing logs, write an efficient algorithm to identify the top bottleneck stages in the pipeline.
Deliverable: Spoken answers to 2 reported Data Structures, Algorithms & Live Coding 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.
Describe a time when a critical system failed in production. How did you diagnose the issue, mitigate the imme
Describe a time when a critical system failed in production. How did you diagnose the issue, mitigate the immediate impact, and prevent it from happening again?
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?
Why do you want to work at Nanonets, and how do you keep up with evolving backend and machine learning infrast
Why do you want to work at Nanonets, and how do you keep up with evolving backend and machine learning infrastructure trends?
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?
Argue against a design, lose, and commit anyway
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
Approach
- State the prediction in falsifiable form up front: the mechanism, the condition that triggers it, and the observable outcome. A prediction that cannot be checked also cannot be credited to you later.
- Show the evidence you had at the time and label each piece honestly as measured, analogous, or intuition. Keeping the intuition is fine; disguising it as data is the thing that erodes your standing in the next argument.
- Represent the opposing case at full strength, including the constraint you did not control: a fixed date, a team boundary, or the fact that the decision was cheap to reverse and yours was not.
- Make disagree-and-commit concrete. Name the artefact you left behind so the prediction could be settled without you: the alert and its threshold, the counter on the dashboard, the decision note that recorded the trade-off and the condition that would revisit it.
Follow-up
- What threshold on that alert would have proved you right, and did anyone ever look at it?
- If the same proposal arrived tomorrow with the same deadline, would you argue it the same way?
- 01
Describe a time when a critical system failed in production. How did you diagnose the issue, mitigate the immediate impact, and prevent it from happening again?
- 02
Why do you want to work at Nanonets, and how do you keep up with evolving backend and machine learning infrastructure trends?
- 03
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
How difficult is the Software Engineer interview at Nanonets?
The interview process is generally rated as average to highly difficult. It is very practical and deeply technical, focusing heavily on live coding, real-world system design, and your ability to write clean, working code rather than just memorizing standard coding puzzles.
Nanonets Software Engineer candidate reports ↗How much preparation time is recommended before the interviews?
It is recommended to dedicate at least 2 to 3 weeks of focused preparation. You should spend this time practicing live scripting (especially API integrations), reviewing object-oriented design principles (LLD), and studying distributed system design patterns (HLD).
Nanonets Software Engineer candidate reports ↗What is the company culture and working style like at Nanonets?
Nanonets operates with a high-performance, high-ownership startup culture. Engineers are given significant autonomy and are expected to take complete responsibility for their projects, from initial design to production deployment and monitoring. Communication and scheduling timelines can sometimes experience delays. If you do not hear back within a week of an interview, do not hesitate to proactively follow up with your recruiter.
Nanonets Software Engineer candidate reports ↗What is the typical timeline from the initial screen to a final offer?
The entire process can move very quickly, sometimes concluding within 2 to 3 weeks. However, because the technical rounds are intensive and often scheduled close together, candidates should be prepared to invest several hours in technical evaluations over a short period.
Nanonets Software Engineer candidate reports ↗How hard is the Nanonets interview?
Candidates most commonly rate Nanonets interviews as medium, based on 74 reported interviews. About 42% of candidates who interview go on to receive an offer.
Nanonets Software Engineer candidate reports ↗What topics does Nanonets test in interviews?
Nanonets interviews most often cover SQL, A/B Testing, Experimentation, Data-Driven Decision Making, and API integration. The exact emphasis depends on the specific role you apply for.
Nanonets Software Engineer candidate reports ↗Is Nanonets a good place to work?
Employees rate Nanonets 3.7 out of 5 overall, based on aggregated workplace reviews spanning career growth, work-life balance, compensation, culture, and management.
Nanonets Software Engineer candidate reports ↗Where is Nanonets headquartered?
Nanonets is headquartered in San Francisco, CA.
Nanonets Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01Nanonets 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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