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Uber·Machine Learning Engineer·Onsite - Multi Round·Senior

SeniorPrefer not to say
Jun 2026

Summary

Uber ML Engineer interview that leaned hard into system design and behavioral depth at the same time. Two meaty questions, both asking you to go end-to-end on real projects rather than toy examples. Left feeling like I either crushed it or completely missed what they were looking for.

Questions Asked (2)

Q1

Walk me through a project where you built an AI function calling system from scratch. Cover the problem, your role, the key technical decisions around APIs and data modeling, the challenges you hit, and what the actual impact was.

System DesignAPI & IntegrationsData Modeling
Author's notes

This one is deceptively open-ended.

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AI HintsAI Generated

Suggested Approach

Use the STAR method to structure your answer, focusing on the problem, your specific role, technical decisions, challenges, and impact. Emphasize how you designed the function calling system, including API design, data modeling, and integration with LLMs, and quantify the impact with metrics.

Pro tip: Highlight trade-offs in your technical decisions, such as choosing between REST and gRPC or how you modeled data for scalability, and tie them back to business outcomes. This shows senior-level thinking.

1. Set the Context and Problem

Briefly describe the project's goal, the specific problem it solved, and why it was important for the business or users.

2. Define Your Role and Responsibilities

Clearly state your role in the project, what you were responsible for, and how you contributed to the overall success.

3. Explain Key Technical Decisions

Detail the architecture, API design (e.g., REST vs. gRPC, schema design), data modeling choices, and how you integrated with LLMs for function calling.

4. Discuss Challenges and Solutions

Describe specific challenges you faced (e.g., latency, accuracy, scalability) and how you overcame them, showing problem-solving skills.

5. Quantify Impact and Learnings

Share the measurable impact of the project (e.g., improved efficiency, cost savings, user engagement) and key takeaways.

Key Points to Mention

  • API design choices: REST vs. gRPC, schema design, versioning, and error handling
  • Data modeling: how you structured data for function calling, including input/output schemas and validation
  • Integration with LLMs: prompt engineering, function calling mechanisms (e.g., OpenAI function calling), and handling model outputs
  • Challenges: latency, accuracy, scalability, and how you addressed them (e.g., caching, batching, fallback mechanisms)
  • Impact metrics: quantify improvements (e.g., reduced manual effort by X%, increased accuracy by Y%, saved $Z)
  • Collaboration: how you worked with cross-functional teams (e.g., product, backend, data science) to deliver the solution

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

Q2

Tell me about a time you hit a serious blocker or conflict while driving a project, like a cross-team disagreement or a priority clash. How did you figure out what was actually wrong, get people aligned, and make the right trade-offs to keep moving?

Conflict ResolutionCross-functional AlignmentStakeholder Management
Author's notes

I actually felt okay about this one.

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AI HintsAI Generated

Suggested Approach

Use the STAR method to structure a story about a cross-team conflict or blocker, focusing on how you diagnosed the root cause, aligned stakeholders, and made trade-offs. Emphasize data-driven decision-making and collaboration, and highlight the positive outcome for the project and the business.

Pro tip: Show that you can separate technical problems from interpersonal ones and that you proactively seek to understand others' priorities and constraints. Demonstrating empathy and a focus on shared goals can set you apart.

1. Set the Context

Briefly describe the project, your role, and the teams involved. Clearly state the blocker or conflict and its impact on the project timeline or goals.

2. Diagnose the Root Cause

Explain how you investigated to uncover the real issue—whether it was misaligned incentives, unclear requirements, or technical constraints. Mention any data or conversations that helped clarify.

3. Align Stakeholders

Describe the steps you took to bring people together, such as organizing a meeting, facilitating a discussion, and finding common ground. Highlight active listening and empathy.

4. Make Trade-offs and Decide

Detail the options considered and the trade-offs made, using data and business impact to justify the decision. Show how you got buy-in from all parties.

5. Execute and Measure

Explain how you implemented the solution, monitored progress, and measured success. Mention any adjustments made along the way and the final outcome.

Key Points to Mention

  • Root cause analysis: distinguishing between technical and interpersonal issues
  • Stakeholder alignment techniques: active listening, empathy, and finding shared goals
  • Data-driven decision-making: using metrics to evaluate trade-offs
  • Trade-off analysis: balancing scope, time, resources, and business impact
  • Communication: keeping stakeholders informed and managing expectations
  • Outcome: positive results for the project and lessons learned for future collaborations

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