Hippocratic AI·Machine Learning Engineer·Onsite - Coding / Algorithms·Senior

Senior

Jun 2026

Summary

Hippocratic AI had me do a live coding round on an actual repo, not a toy sandbox, which was a different kind of pressure than I expected for an MLE role. The whole thing tested whether you could orient yourself fast, make a targeted change, write tests, and lean on AI tooling without letting it make a mess of things.

Questions Asked (1)

Given an unfamiliar live repository, how would you orient yourself quickly, locate the right area to change, implement a small feature or bug fix, verify it with tests, and explain the trade-offs you made, all within a strict time limit while using AI coding assistants appropriately?

Technical Trade-offsAdaptability & AmbiguityAPI & Integrations

Author's notes

The part that tripped me up was the orientation phase.

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AI HintsAI Generated

Suggested Approach

Walk through a structured, time-boxed process: first map the repo and identify entry points, then trace the relevant code path, make a minimal change, and verify with targeted tests. Emphasize how you use AI assistants to accelerate each step while critically validating their output, and explicitly state the trade-offs you made under time pressure.

Pro tip: Show that you treat AI suggestions as hypotheses to test, not answers to trust—run the tests and read the diff before committing. Also, mention that you time-box exploration and switch to implementation once you have enough context, because perfection is the enemy of shipping.

1. Orient and map the repository

Spend the first few minutes scanning the README, directory structure, and key configuration files to understand the project's purpose, tech stack, and entry points. Use AI to summarize the repo or explain unfamiliar files, but verify critical details yourself.

2. Locate the relevant code area

Trace from the entry point or failing test to the specific module, using search tools and AI-assisted code navigation. Identify the smallest surface area that needs to change and confirm your understanding by reading the surrounding code and tests.

3. Implement a minimal, testable change

Write the smallest possible change that addresses the feature or bug, following existing patterns and style. Use AI to draft code or suggest fixes, but review and adapt it to the codebase's conventions and constraints.

4. Verify with targeted tests

Run the most relevant unit or integration tests first, then broaden if time permits. If tests are missing, write a quick test to validate the change, and use AI to help generate test cases while ensuring they actually test the behavior.

5. Explain trade-offs and reflect

Articulate the trade-offs you made: speed vs. completeness, scope of change, reliance on AI vs. manual verification, and any technical debt incurred. Be honest about what you would do differently with more time.

Key Points to Mention

Time-boxing exploration and using a 'good enough' threshold to move to implementation
Leveraging AI for code summarization, search, and boilerplate, but validating with tests and manual review
Prioritizing the smallest viable change to reduce risk and enable faster verification
Running targeted tests first, then expanding coverage if time allows
Communicating trade-offs clearly, such as choosing a quick fix over a refactor or accepting limited test coverage
Demonstrating adaptability by adjusting the plan based on new information from the code or tests

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.