Describe past impact and conflict handling
Overview
This question evaluates a candidate's competency in end-to-end AI function-calling implementation, covering technical decision-making, API and data design, tooling and observability, measurable impact, and leadership in conflict resolution, and is categorized under Behavioral & Leadership for a Machine Learning Engineer role.
Uber
Sep 6, 2025, 12:00 AM
Machine Learning Engineer
Onsite
Behavioral & Leadership
0
0
Behavioral and Leadership: AI Function Calling End-to-End + Conflict Resolution
Context
You are interviewing for a Machine Learning Engineer role. The interviewer asks you to demonstrate end-to-end ownership of an AI function-calling project and to show how you lead through ambiguity and conflict.
Prompt
- Walk through a past project where you implemented AI function calling end-to-end. Cover:
- Problem context and why function calling was the right approach
- Your role and ownership boundaries
- Key technical decisions (LLM/provider, API design, orchestration, data modeling, schemas, logging)
- Tooling/stack (frameworks, infra, evals, monitoring, CI/CD)
- Main challenges and how you addressed them (e.g., JSON brittleness, latency, hallucinations, privacy)
- Measurable impact with concrete metrics
- Describe a time you faced conflicts or blockers while driving this project (e.g., cross-team priorities, design disagreements):
- Root-cause diagnosis
- How you aligned stakeholders and made trade-offs
- Actions you took to move forward and the outcome
- What you would do differently in hindsight
Aim for a crisp, structured narrative with specific metrics and decisions (8–10 minutes).
Solution
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