← Harvey AI Interview Insights
I had something prepped but the follow-up questions are where it gets uncomfortable.
Select a project where you made key technical decisions and can articulate the trade-offs. Structure your answer to show how you balanced model performance with product metrics, and highlight collaboration with cross-functional teams. Emphasize the impact on end-users and business outcomes.
Pro tip: Quantify the impact of your work using metrics that matter to the business, such as accuracy improvements, latency reductions, or cost savings. Also, briefly mention a lesson learned or a trade-off you'd revisit, showing self-awareness and growth mindset.
Briefly describe the project's goal, your role, and the team composition. Mention the problem it solved and why it mattered.
Walk through the key technical choices you made, such as model selection, feature engineering, or infrastructure. Highlight alternatives considered and why you chose your approach.
Articulate the trade-offs you navigated, such as accuracy vs. latency, cost vs. performance, or complexity vs. maintainability. Explain how you balanced them.
Describe how you measured success using product and business metrics. Explain how you iterated based on these metrics and collaborated with product teams.
Summarize the results with quantifiable impact. Reflect on what you learned and how you'd approach it differently next time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Choose a specific ML project where you evaluated multiple technical options, and walk through the decision-making process. Highlight the trade-offs you weighed and how you validated your final choice, tying it back to business impact and constraints. Show that you can balance technical rigor with pragmatism.
Pro tip: Emphasize that you considered not just model performance but also operational factors like latency, cost, and maintainability—this shows you think like a product-minded engineer, which is crucial at a startup like Harvey AI.
Briefly describe the project, its goals, and the key constraints (e.g., data size, latency requirements, budget). This helps the interviewer understand the decision environment.
Mention 2-3 distinct technical approaches you considered, such as different model architectures, libraries, or infrastructure. Explain why each was a viable option.
Compare the alternatives on dimensions like accuracy, training time, inference speed, scalability, cost, and team expertise. Use concrete metrics or estimates where possible.
State which approach you chose and why it best met the project's needs. Highlight any prototyping or experiments that validated your choice.
Share the results of your choice and what you would do differently next time. This shows self-awareness and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Harder than it sounds when you're the one who built the thing.
Choose a specific ML project where you made a key technical decision, and structure your answer to clearly state the problem, the approach, its pros and cons, and the measured outcome. Be honest about limitations and show how you validated the solution with metrics and user feedback.
Pro tip: Quantify the trade-offs and results with concrete metrics (e.g., latency, accuracy, cost) and tie them back to business impact, such as improved user engagement or reduced operational costs. This demonstrates product thinking and engineering maturity.
Briefly describe the problem, the constraints, and why it mattered to the business or users. Keep it concise to focus on the trade-offs.
Summarize the technical approach you took, including key design decisions and alternatives considered. Highlight why you chose this path.
List the main advantages and disadvantages of your approach, comparing it to alternatives. Be specific about trade-offs in performance, cost, complexity, and maintainability.
Present how well the solution solved the problem using quantitative metrics (e.g., accuracy, latency, ROI) and qualitative feedback. Acknowledge any gaps or unexpected results.
Share what you learned and how you would improve the approach next time. This shows growth mindset and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.