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Expedia·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

One round at Expedia for an ML Engineer role, basically just a walkthrough of my own projects. Pretty low-key as far as technical screens go.

Questions Asked (1)

Q1

Walk me through a project you've worked on recently. What was your role and what technical decisions did you make?

Technical Trade-offsAdaptability & Ambiguity
Author's notes

They just wanted me to talk through my own work, which sounds easy until you're actually doing it live and second-guessing every detail you mention.

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AI HintsAI Generated

Suggested Approach

Choose a recent ML project that showcases your technical decision-making and ability to handle ambiguity. Structure your answer with context, your specific role, key technical decisions and trade-offs, and the impact. Highlight how you navigated uncertainty and adapted your approach.

Pro tip: Quantify the impact of your technical decisions (e.g., 'reduced inference latency by 30%') and briefly mention an alternative you considered but rejected, explaining why. This demonstrates depth and trade-off analysis.

1. Set the Context

Briefly describe the project's goal, the business problem, and why it mattered. Mention the team size and your specific role.

2. Explain Your Role and Responsibilities

Clarify what you were directly responsible for, including any leadership or collaboration aspects. Be specific about your contributions.

3. Detail Key Technical Decisions

Walk through 2-3 critical technical decisions you made, such as model selection, feature engineering, or deployment strategy. Explain the rationale and trade-offs.

4. Discuss Challenges and Adaptability

Describe a significant challenge or ambiguity you faced and how you adapted. Highlight any pivots or learnings.

5. Summarize Impact and Learnings

Conclude with the measurable outcomes of your work and what you learned. Connect it to the role at Expedia if possible.

Key Points to Mention

  • Model selection trade-offs (e.g., accuracy vs. latency, complexity vs. interpretability)
  • Data preprocessing and feature engineering decisions
  • Evaluation metrics and validation strategy
  • Deployment and scalability considerations
  • Collaboration with cross-functional teams (e.g., product, data engineering)
  • Quantifiable impact (e.g., improved conversion, reduced costs)

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