Uber·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

First round at Uber for an MLE role. It was a project deep dive and things went sideways pretty fast once I realized my project wasn't really what the team was looking for.

Questions Asked (1)

Walk me through a recent project you worked on that you feel represents your work well.

System DesignTechnical Trade-offsProduct Strategy

Author's notes

I went with a GenAI agent project because I thought it showed breadth: business context, system design, data pipeline, eval, post-launch tradeoffs.

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AI HintsAI Generated

Suggested Approach

Select a project that showcases end-to-end ML system design, from problem definition to deployment and iteration, with clear business impact. Structure your answer using a narrative arc: context, problem, approach, trade-offs, results, and learnings. Emphasize collaboration with cross-functional teams and how you navigated technical and product constraints.

Pro tip: Quantify impact with metrics that matter to Uber (e.g., improved ETA accuracy by X%, reduced inference latency by Y ms, increased driver utilization by Z%). Also, briefly mention a failure or iteration that led to a better solution—it shows humility and a growth mindset.

1. Set the Context

Briefly describe the project's goal, your role, and the team composition. Highlight the business problem and why it mattered.

2. Explain the Technical Approach

Outline the ML system design: data sources, feature engineering, model choice, training pipeline, and deployment architecture. Focus on key decisions and alternatives considered.

3. Discuss Trade-offs and Challenges

Detail the trade-offs you made (e.g., latency vs. accuracy, complexity vs. maintainability) and how you overcame obstacles like data quality or scalability.

4. Share Results and Impact

Present measurable outcomes: model performance, business metrics, and system reliability. Connect these to broader company goals.

5. Reflect on Learnings

Summarize what you learned, what you would do differently, and how it influenced your subsequent work.

Key Points to Mention

Problem framing and alignment with business objectives (e.g., reducing ETAs, optimizing pricing, fraud detection).
Data pipeline and feature engineering at scale (e.g., using Kafka, Spark, or Uber's Michelangelo).
Model selection and evaluation metrics (e.g., why you chose a particular algorithm, how you validated it).
Deployment and monitoring (e.g., A/B testing, canary releases, handling model drift).
Cross-functional collaboration (e.g., working with product managers, data scientists, backend engineers).
Quantified impact (e.g., % improvement in key metric, cost savings, latency reduction).

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