This one took up a lot of time and I don't think I paced it well.
Choose a single ML system you owned end-to-end and narrate it as a decision story, not a feature list. Walk through each phase (framing, data, modeling, online/offline, evaluation) and explicitly call out the trade-offs you made and why. Close with what you'd change now, showing growth and systems thinking.
Pro tip: At Uber's scale, emphasize how your system handled real-time constraints, marketplace dynamics, and experimentation infrastructure—quantify impact with business metrics (e.g., ETA accuracy, conversion lift) and mention how you'd re-architect for today's scale or new constraints.
Define the user/business problem, why ML was needed, and the success metrics (offline and online). Clarify constraints like latency, scale, and fairness.
Describe data sources, volume, labeling strategy, and key features. Highlight challenges like skew, leakage, or real-time feature computation.
Explain model choice, training pipeline, and online serving architecture. Discuss trade-offs between model complexity, latency, and maintainability.
Cover offline metrics, online A/B testing, guardrail metrics, and how you validated causal impact. Mention monitoring and feedback loops.
Share what you'd change now—e.g., better feature store, model retraining cadence, or handling distribution shift—and why.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I had a decent story here but I realized partway through I was underselling the actual disagreement.
Choose a real technical disagreement where you prioritized data and user impact over being right, and show how you separated the person from the problem. Walk through how you listened, surfaced assumptions, and used experiments or metrics to converge on a decision. End with the outcome and what you learned about collaborating on ML trade-offs at scale.
Pro tip: Frame the conflict as a shared search for truth: name the specific metric or constraint that resolved it, and credit your teammate for the insight that changed your mind or improved the final design. This signals low ego and high technical judgment, which Uber values in ML engineers.
Briefly describe the project, your role, and why the technical decision mattered (e.g., model latency vs. accuracy affecting rider ETA predictions). Keep it to 2-3 sentences so the interviewer understands the trade-off.
State both positions fairly and identify the root cause: different assumptions about data drift, offline vs. online metrics, or cost constraints. Show you understood your teammate's reasoning, not just your own.
Explain how you scheduled a focused discussion, used data or a small experiment to test hypotheses, and actively listened. Emphasize curiosity and separating technical critique from personal criticism.
Describe how you reached a decision—whether you converged on one approach, ran an A/B test, or split the difference with a fallback. Include the measurable impact (e.g., reduced latency by X% without hurting AUC).
State what you took away about collaboration, decision-making under uncertainty, or ML trade-offs, and how you've applied it since. Keep it concise and forward-looking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a story about a modeling approach my manager wanted to ship faster than I thought was safe.
Choose a disagreement where you had concrete data and a clear alternative, and frame it as a collaborative problem-solving exercise rather than a personal conflict. Walk through how you presented evidence, listened to the stakeholder's concerns, and reached a resolution—even if it meant a compromise or deferring to their call with a plan to validate later.
Pro tip: Emphasize that you disagreed on the 'how' or 'when,' not the 'what'—showing you aligned on the ultimate business goal makes you look like a team player, not a contrarian. Also, mention what you learned from the stakeholder's perspective, demonstrating humility and growth.
Briefly describe the project, your role, and why the disagreement mattered—tie it to a business metric like model accuracy, latency, or revenue impact.
State your position and the stakeholder's position without blame, focusing on the technical or strategic trade-offs (e.g., model complexity vs. interpretability, speed vs. accuracy).
Describe the specific data, experiments, or prototypes you brought to the table, and how you framed the trade-offs in terms of business outcomes and risk.
Explain how you listened to their concerns, sought common ground, and either persuaded them, compromised, or agreed to a test—highlighting collaboration and respect.
Conclude with the result (e.g., improved metric, faster iteration) and what you learned about stakeholder management or decision-making.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Hardest of the three behavioral questions for me.
Choose a real disagreement where you either resolved it through data-driven discussion or committed to a decision despite reservations. Structure your answer using a clear narrative: context, the nature of the disagreement, your decision-making process, the outcome, and what you learned. Emphasize how you balanced technical rigor with team goals and business impact.
Pro tip: Show that you can disagree and commit without ego—highlight how you supported the final decision even if it wasn't yours, and quantify the outcome to demonstrate your focus on results.
Briefly describe the project, your role, and the stakeholders involved. Make sure the disagreement is relevant to ML engineering and cross-functional collaboration.
Clearly state the opposing viewpoints, who held them, and why the disagreement mattered. Focus on technical or strategic differences, not personal conflicts.
Describe how you evaluated the options: what data you gathered, experiments you ran, or trade-offs you considered. Explain the criteria you used to decide whether to resolve or commit.
Explain the path you took and why. If you resolved it, show how you aligned the team. If you committed, demonstrate how you supported the decision and mitigated risks.
Quantify the impact (e.g., model performance, business metrics) and reflect on what you learned about collaboration, decision-making, or technical judgment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.