harvey Software Engineer Interview: Questions & Prep (2026)

Harvey is an AI company building tools for lawyers and professional-services firms, contract analysis, legal research, and document drafting powered by large language models. With 344 open Software Engineer roles as of July 2026 (knok jobradar), the company is hiring aggressively across backend, ML, and full-stack tracks. The interview process typically runs 3-5 rounds: a recruiter screen, one or two technical coding rounds, a system-design discussion, and a final values or culture-fit conversation. Candidates report that Harvey leans on real-world problem-solving rather than purely abstract puzzles, though solid fundamentals in data structures and algorithms are still expected. Because Harvey's product operates in the legal domain, where errors have real consequences, expect questions that probe how you think about reliability, accuracy, and auditability in AI systems. Salary bands for Software Engineers in India (2026):

These questions come up most often, based on what candidates report across interview feedback platforms: 1. Walk me through how you would design a system that ingests and indexes thousands of legal documents per hour.
2. How would you build a reliable job queue for long-running LLM inference requests? What happens when a job times out or fails mid-way?
3. Harvey serves multiple law firms from one platform. How would you design a multi-tenant API that keeps each client's data isolated without sacrificing performance?
4. How would you handle versioning for LLM outputs so that downstream features stay stable when the underlying model is updated?
5. How do you write tests for non-deterministic outputs, for example, AI-generated contract summaries where the exact wording can vary on each call?
6. Describe your experience with embeddings or vector search. What trade-offs did you face when choosing an approach?
7. A customer reports that a clause extracted from their contract was wrong. Walk us through how you would debug and fix that end-to-end.
8. How would you rate-limit and prioritise calls to a third-party LLM provider when you have many tenants with different SLA tiers?
9. Tell me about a time you improved the performance of a slow API or database query, what did you measure, and what did you change?
10. Tell me about a time you disagreed with a technical decision. What did you do, and what was the outcome?
11. How do you keep a codebase readable and maintainable as a product grows from prototype to production scale?
12. Where do you see AI in legal services heading over the next few years, and how does that shape the software you write today?

Use the STAR format, Situation, Task, Action, Result, for every behavioural question. Keep Situation and Task brief; spend most of your time on Action and Result. --- Q: Tell me about a time you improved the performance of a slow system. *Situation:* At my previous company, our document-parsing service was causing frequent timeouts for users who uploaded large batches of files. *Task:* I was asked to reduce end-to-end latency without rewriting the core parsing logic, which was owned by another team. *Action:* I profiled the service and found that sequential I/O was the main bottleneck, each page was fetched and processed one at a time. I refactored the pipeline to use async I/O with a worker pool, added caching for repeated file hashes, and moved thumbnail generation to a background job so it no longer blocked the main response. *Result:* Median latency dropped significantly. I shipped the change with zero regressions after writing integration tests covering the new async code paths. --- Q: Describe a time you worked on a system with non-deterministic outputs. *Situation:* I built a feature that used an LLM to extract key clauses from contracts. The same document could return slightly different text on each call, making traditional unit tests unreliable. *Task:* I needed a testing and monitoring strategy that would catch regressions whenever the model or prompt was updated. *Action:* I created a golden-set evaluation: a curated set of contracts with human-verified expected outputs. For each model update I ran the suite and tracked a semantic-similarity score rather than exact string matching. I also added production logging to flag outputs below a confidence threshold for human review. *Result:* When the LLM provider released a model update, we caught a clause-extraction regression within hours rather than days and rolled back before it affected more than a small fraction of users. --- Q: Tell me about a time you disagreed with a technical decision. *Situation:* My team chose a single shared database for a new multi-tenant feature. I was concerned the access patterns would cause lock contention as we scaled. *Task:* I needed to raise the concern without blocking a deadline that was two sprints away. *Action:* I prepared a short written comparison of the two approaches, shared DB vs. schema-per-tenant, with latency projections backed by a load test I ran over a weekend. I shared it in the design review and proposed a phased plan: ship on the shared DB now, then migrate to schema-per-tenant the following quarter. *Result:* The team adopted the phased approach. We shipped on schedule, and the migration completed one quarter later with no customer downtime.

STAR, Situation → Task → Action → Result
The standard for every 'tell me about a time' question. Keep Situation and Task to two or three sentences combined. Load most of your answer into Action, what *you* specifically did, not what the team did, and Result, with measurable outcomes wherever possible. --- Problem → Constraints → Options → Decision
Use this for system-design and architecture questions. First restate the problem in your own words. Then name the real constraints, latency, cost, data volume, team size. Walk through two or three design options with their trade-offs. End with what you would choose and why. Jumping straight to a solution without naming constraints is one of the most common mistakes interviewers flag. --- Observe → Measure → Hypothesise → Fix → Verify
Use this for debugging and performance questions. Harvey interviewers typically want to see a structured, evidence-driven process, not a list of things to 'try' at random. --- AI-specific tip: For any question involving a model or LLM feature, always address evaluation, monitoring, and rollback. In a legal context, a wrong answer is not just a bad user experience, it can have real consequences. Showing you think about failure modes signals engineering maturity.

Based on publicly available interview feedback, Harvey interviewers typically look for four qualities: 1. Engineering depth, Can you reason about systems under real constraints? They want to see how you think through trade-offs in production, not just that you know the right keywords. 2. AI and ML literacy, You do not need to be a researcher, but you should be comfortable with concepts like embeddings, vector search, retrieval-augmented generation (RAG), and the challenges of deploying non-deterministic models reliably at scale. 3. Ownership and initiative, Harvey is a fast-moving startup. Interviewers want evidence that you drive problems to resolution rather than wait to be assigned a fix. 4. Clear, precise communication, The product serves lawyers, who value exactness. Candidates who can explain complex technical decisions clearly and without jargon tend to stand out. Prior legal-tech experience is not required. Showing genuine curiosity about how AI transforms knowledge work, and the unique challenges of deploying AI in a high-stakes domain, goes a long way.

Week 1, Coding Fundamentals
- Revise data structures and algorithms with emphasis on string processing, trees, and graphs, these come up often in document-oriented problems.
- Solve 2-3 medium-difficulty coding problems per day, focusing on writing clean, well-named code rather than just getting the answer.
- Review async programming patterns in your language of choice: async/await, worker pools, message queues. Week 2, System Design and AI Systems
- Study distributed system patterns: job queues, rate limiting, caching, idempotency, and retry logic.
- Read about vector databases and RAG pipelines, these are directly relevant to what Harvey builds.
- Practise designing one AI-powered system end-to-end: for example, a document Q&A service for a law firm covering ingestion, indexing, querying, and response generation. Week 3, Behavioural and Company Research
- Write STAR stories for: improving performance, resolving a disagreement, debugging a hard bug, and shipping under pressure.
- Try Harvey's product, sign up for a trial or watch demos, so you can speak specifically about what you find interesting.
- Prepare 3-4 questions for your interviewers about team structure, how they evaluate model quality in production, and on-call responsibilities. Day before
- Review your STAR stories once. Do not cram new technical material.
- Harvey interviews are typically conversational, being sharp and well-rested matters more than squeezing in one more system-design diagram. If you are applying to Harvey alongside other roles, knok checks 150+ job sites nightly, applies to jobs that match your resume, and messages HR on your behalf, so you can spend your energy on interview prep rather than job hunting.

1. Jumping to code before clarifying requirements. Candidates report that Harvey interviewers expect you to ask about scale, edge cases, and constraints before writing anything. Skipping this signals you do the same in real work. 2. Treating LLM outputs as deterministic. If you discuss any AI feature, always mention how you would test, monitor, and handle failures. Saying 'the model will return the right answer' is a significant red flag in a legal-tech context. 3. Giving generic system-design answers. Saying 'use Kafka' or 'add a cache' without explaining why, for Harvey's specific scale, latency, and cost constraints, reads as shallow preparation. 4. Underselling your ownership. Harvey is a startup. If you drove a problem from identification through fix to production, say exactly that. Framing yourself as a ticket-executor when you were actually the decision-maker costs you points. 5. Not knowing the product. Candidates report that interviewers ask why you want to work at Harvey specifically. A vague answer like 'AI is exciting' is not enough, show you understand what legal-tech engineers are actually trying to solve and why it is genuinely hard. 6. Ignoring edge cases in coding rounds. Empty inputs, very large documents, Unicode characters in legal text, network timeouts, proactively mentioning these, even if you do not implement all of them, signals production-level thinking.