Updated July 3, 2026 · techinterview.org

Harvey Interview Guide

Company overview: Harvey is the leading vertical AI platform for the legal industry, used by major law firms (Allen & Overy, PwC, Cravath, etc.) for legal research, contract analysis, document drafting, and litigation support. Founded in 2022; San Francisco headquarters with engineering in SF and remote. Backed by OpenAI and major VCs; one of the highest-profile vertical AI startups in 2026.

Interview process

Timeline: 3–5 weeks.

1. Recruiter screen. A short call covering your background, why Harvey, and logistics like level and location; treat it as a filter and have a crisp two-minute story about why legal AI interests you.
2. Hiring manager screen (45 min). Background, motivation for legal AI, role fit.
3. Technical phone screen (60 min). A coding problem, usually a medium solved in a shared editor. Talk through your approach before writing code and test your solution on a small example.
4. Virtual onsite (4–5 rounds).
   • 1–2 coding rounds (medium difficulty). Expect array, string, and hash-map problems in Python, judged on clean working code and clear communication more than exotic tricks. Drilling medium problems until the common coding patterns are automatic is enough preparation.
   • 1 system design round (often LLM-application architecture). Instead of the classic “design Twitter” prompt, you’re more likely to design a RAG pipeline, a document-processing service, or an agent workflow. Interviewers probe how you handle retrieval quality, latency, and cost at the scale of large law-firm document sets.
   • 1 ML / LLM application round for relevant tracks. This round tests practical LLM engineering: prompt design, evaluation, and knowing when fine-tuning beats retrieval. Be ready to discuss how you’d measure output quality and catch hallucinations rather than model-training internals.
   • 1 behavioral / culture round. Harvey looks for people who care about the product and can work with non-engineers like lawyers and product staff. Prepare stories about ownership, ambiguity, and shipping fast, and be able to explain why legal AI pulls you in.
5. Founder interview for senior+ roles. Expect questions about product vision, why you want to build in legal AI, and how you think about the company’s bets; this is as much a mutual-fit conversation as an evaluation.

Common technical questions

• Standard LeetCode mediums (Python dominant). These are conventional data-structure and algorithm questions, so know Python idioms like comprehensions, dictionaries, sets, and the collections module. Point your prep at arrays, strings, hash maps, and two-pointer patterns rather than obscure graph or DP problems.
• LLM application architecture: prompt engineering pipelines, RAG over legal documents, output verification, citation grounding. Interviewers want to see that you can turn a raw model into a reliable product feature, so be ready to explain how you’d ground every answer in source text and verify outputs before a lawyer ever reads them.
• Document-processing pipelines: PDF parsing, structured extraction, OCR for scanned documents. Legal documents are messy — scanned PDFs, dense tables, footnotes, and inconsistent formatting are the norm. Expect to walk through how you’d extract structured fields reliably and handle the long tail of files that break a naive parser.
• Evaluation: how to evaluate legal AI outputs (correctness, hallucination, citation accuracy). This is one of Harvey’s hardest problems, so it comes up often; have a concrete answer for how you’d score quality at scale without a lawyer reviewing every response — think rubrics, pairwise comparisons, and automated citation checks.
• For senior+: enterprise security and compliance for handling client-confidential documents. Law firms hand over privileged material, so isolation and access control are non-negotiable. Be ready to talk through tenant separation, encryption, and why firms often demand single-tenant or virtual-private-cloud deployments.

The legal-domain context

Working at Harvey requires comfort with the legal domain even if you are not a lawyer. The engineering work is informed by what lawyers actually do — drafting briefs, analyzing contracts, conducting discovery — and the product judgment expected of senior engineers includes understanding the workflows of practicing attorneys. Candidates without any familiarity with legal work often struggle to make good product decisions; some background reading on legal practice helps interview performance.

Compensation (2026 estimates, San Francisco)

• Mid: $180–230K base + significant equity + bonus → $300–450K total
• Senior: $230–290K base + significant equity → $450–650K total
• Staff: $290–370K base + substantial equity → $650K–950K total

Harvey is private; equity has appreciated significantly given recent funding rounds and customer growth.

Sample interview questions in depth

Coding (Python-heavy LLM application)

• Build a contract-extraction pipeline. Given a 200-page master service agreement, extract structured data (parties, term, payment schedule, liability caps, governing law). Discuss chunking strategies, how to handle conflicting clauses, and how to evaluate extraction accuracy.
• Implement citation grounding. Every legal-AI answer must point back to the source paragraphs in the underlying document. Discuss how to do this without hallucinated citations: span-extraction models, retrieval-then-cite patterns, and the role of the LLM as orchestrator vs source-of-truth.
• Design a redlining engine. Compare two contract drafts and produce a clean, lawyer-readable diff with semantic awareness (this paragraph was rewritten, that obligation moved sections). Pure text diff is insufficient because legal language is restructured during negotiation.

LLM application architecture

• RAG over law-firm document collections: chunking, embedding model choice, retrieval-quality evaluation, the role of reranking. Discuss why naive RAG underperforms in legal contexts (precedent matters, jurisdiction matters).
• Multi-step agent workflows: how Harvey’s research-assistant feature chains retrieval, reasoning, drafting, and citation-checking. The cost of long agent traces and how to keep them debuggable.
• Evaluation infrastructure: how to assess legal-AI quality without an army of in-house lawyers. Pairwise comparison, rubric-based scoring, the role of customer feedback loops.

Enterprise security and compliance

• Handling client-confidential documents: tenant isolation in vector stores, encryption at rest and in flight, attribute-based access control. Why most law firms require on-premise or virtual-private-cloud deployments.
• SOC 2 Type II: what auditors look for, how engineering practices map to controls, the operational overhead.
• Privilege-aware access controls: matter-level isolation (information about Case A must not leak to a lawyer working on Case B even within the same firm). Conflict checks at retrieval time.

The legal-domain context

Harvey works with major law firms (Allen & Overy, Cravath, Latham & Watkins, PwC). The product judgment expected of senior engineers includes understanding what lawyers actually do day-to-day: due diligence on M&A transactions, drafting and negotiating contracts, conducting legal research, analyzing case law. Candidates who have worked adjacent to legal services (legal-tech, regulatory technology, e-discovery) have a leg up. Pure SaaS engineers without legal-domain familiarity should expect to do background reading: a chapter or two from a contracts or torts textbook, plus reading Harvey’s blog posts on customer use cases.

The OpenAI relationship

Harvey is one of the most prominent companies built on top of OpenAI’s models, with OpenAI as both an investor and infrastructure provider. This creates both strengths (early access to model capabilities) and constraints (architecture decisions are influenced by OpenAI’s roadmap). Engineering interviews sometimes probe whether the candidate has thought about model-provider risk and what a multi-model strategy would look like.

Frequently Asked Questions

Do I need legal background?

No, but legal-domain interest helps. Harvey hires across engineering tracks; lawyers are involved in product but engineers don’t need to be lawyers.

What languages are used?

Python dominant for ML and backend; TypeScript for frontend; some Go for infrastructure. Standard modern AI-app stack.

How does Harvey compare to other vertical AI startups?

Harvey is the largest legal-AI player. Competitors include Casetext (acquired by Thomson Reuters), Lexis+ AI (LexisNexis), and emerging startups in specific legal sub-verticals. Harvey has the strongest brand and customer base.

Adjacent AI / ML Tooling Companies

• Glean — enterprise AI search
• Weights & Biases — ML experiment tracking
• Character.AI — consumer AI
• OpenAI — AI research and Harvey investor

What the Role Pays and How to Weigh the Equity

Compensation at a company at Harvey’s stage usually arrives in two parts: a cash base that lands close to, but often a notch below, what the largest public tech firms pay for the same level, and an equity grant that does most of the work on total value. The split moves with your level, your location, and how recently the company raised, so treat any number you see on a comp aggregator as a starting point and confirm it against current data and your own offer letter. The figure that matters most is the one nobody can promise you, which is what the equity ends up being worth.

At this stage you are most likely offered stock options rather than RSUs, though some later-stage private companies switch to RSUs once a liquidity path looks close. Options carry a strike price, the amount you pay to turn each option into a share, and your gain is whatever the share is worth above that strike, after tax. Two forces shape the real value. Dilution means later funding rounds issue new shares, so your slice shrinks even when the grant on paper does not. Illiquidity means private shares are hard to sell, so the value stays theoretical until an acquisition or IPO that may be years out or may never come. The downside is real: options can expire worthless if the company stalls or the eventual price sits below your strike. The upside is also real, since one grant in a company that breaks out can pay more than several years of big-tech stock.

	Big-tech RSUs	Early-stage / scaleup options
Liquidity	Sellable soon after vesting	Locked until an exit
Predictability	Known schedule, plannable	Depends on an outcome you don’t control
Range of results	Narrow	Zero to very large

The equity terms are where the negotiating room usually sits. If you can carry the risk, ask to shift some cash into more equity, or do the reverse if you would rather lock in certainty. Pin down the details the headline number hides: the strike price and the current preferred share price, the size and cadence of refresh grants after the first vest, and the cliff, since the standard one-year cliff means leaving before month twelve walks away with nothing. Ask what happens to vested and unvested options when you leave, and how long you have to exercise afterward, because a short post-termination window can force an expensive decision on little notice.