Harvey's Interview Process (2026)

Blog / Harvey's Interview Process (2026)
Harvey Interview Process
Harvey's software engineer interview process is highly selective and typically spans 3 to 5 weeks, blending core algorithms with practical AI application questions specific to the legal domain.
  • Recruiter Screen: A short call, usually around 15 to 30 minutes, covering your background, interest in legal AI, and general fit for the role.
  • Hiring Manager Screen: A deeper technical conversation, typically around 45 minutes, where you discuss your engineering background and motivation for joining a high-growth AI startup.
  • Technical Phone Screen: A live coding session, usually around 60 minutes, focused on data structures and problem-solving in a collaborative editor. Python is most commonly used.
  • Virtual Onsite: Generally 4 to 5 rounds covering coding, system design, product and AI thinking, and a behavioral culture discussion. Most candidates report receiving feedback within 48 hours of each stage.
  • Founder / Executive Interview: Typically reserved for Senior and above roles, this round focuses on long-term strategic and cultural alignment with the company's mission.
To prepare effectively, focus your study plan on these key areas that Harvey consistently tests across its interview rounds:
  • Data Structures & Algorithms (DSA): Focused on graphs, trees, linked lists, and practical coding tasks like retry logic and file system design.
  • System Design: Focused on designing large-scale document processing systems, RAG pipelines, and fault-tolerant architectures for legal data.
  • Legal AI & Product Thinking: Focused on applying engineering thinking to legal workflows, LLM output verification, and AI application architecture.
  • Behavioral: Focused on ownership, execution, and collaboration in a fast-paced startup environment.
1. Data Structures & Algorithms (DSA)Harvey's coding rounds go beyond standard array and string problems. Expect a heavy focus on graphs and trees, including more advanced structures like R-trees used for document partitioning.Practical implementation tasks are common. You might be asked to build a "Function Retryer" system with exponential backoff and async compatibility, or implement a file storage system with path-based operations similar to Design In-Memory File System.Linked list problems also appear regularly. A classic like Reverse Linked List is fair game, so make sure you have the fundamentals locked in alongside the more complex graph work.For structured preparation, work through our top 100 DSA questions and pay particular attention to graph traversal and tree manipulation problems. Python proficiency is expected, and familiarity with asyncio or threading will give you an edge in coding tasks that simulate document processing pipelines.
2. System DesignThe system design round at Harvey typically focuses on large-scale document processing and AI-integrated architectures. A common prompt is designing a RAG (Retrieval-Augmented Generation) pipeline over millions of legal documents, with an emphasis on citation accuracy and data integrity.You should be comfortable discussing database query optimization, fault tolerance, and how to handle sensitive, client-confidential data at scale. Legal clients have extremely low tolerance for data leaks or unverified outputs, so security and correctness should be front and center in your designs.Brush up on High-Level Design concepts and practice drawing out architectures using our System Design AI Whiteboard. Thinking through how components like vector databases, embedding stores, and LLM APIs interact will serve you well here.
3. Legal AI & Product ThinkingHarvey includes a dedicated round that evaluates how you apply engineering to real legal product problems. Expect questions like "How would you evaluate the correctness of an AI-generated legal brief?" or "How do you handle hallucination checks in a high-stakes legal context?"You do not need a legal background, but you should be able to speak to legal workflows like contract analysis, discovery, and drafting. Interviewers want to see that you understand why correctness and auditability matter in this domain, not just that you can build fast.Some coding tasks in this area ask you to simulate ML model outputs using tree or graph algorithms, testing your understanding of the logic behind AI features without calling an actual model. Studying system design core concepts and thinking about prompt versioning and output verification strategies will prepare you well for these questions.
4. BehavioralHarvey's behavioral round centers on ownership, execution speed, and collaboration in a startup setting. Interviewers are looking for candidates who take initiative and can operate with minimal hand-holding in a high-growth environment.Prepare specific examples that show how you have driven a project end-to-end, handled ambiguity, or delivered under pressure. Structure your answers using the STAR principle to keep your responses focused and concrete.For broader preparation, the Behavioral Interview Course and Behavioral Playbook are solid resources to work through before your onsite.
ConclusionHarvey moves quickly, so start your preparation early and focus on graphs, trees, and system design with an AI and legal context in mind. For a structured, step-by-step plan covering every stage of the process, follow the Harvey Interview Roadmap and work toward your offer with a clear strategy.

About TechPrep

Never walk into a technical interview unprepared again. TechPrep empowers software engineers to stop guessing and start getting offers. We provide the exact questions asked by tech companies across Data Structures & Algorithms, System Design, Low-Level Design & Practical coding rounds. Don't leave your career up to chance. Join thousands of engineers who have successfully navigated the tech hiring maze and landed roles at top tech companies.