Roblox·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Went through a project presentation round for an MLE role at Roblox and it was kind of a weird experience. I kept pausing between slides to invite questions and got nothing, just silence. The only thing the interviewer asked at the very end was a token question about my role, which felt more like a formality than actual curiosity.

Questions Asked (1)

What was your specific role and contribution in the project you presented?

Cross-functional AlignmentAdaptability & Ambiguity

Author's notes

Asked at the very end, clearly as an afterthought.

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AI HintsAI Generated

Suggested Approach

Start by clearly stating your specific role and the scope of your responsibilities, then highlight 2-3 concrete contributions that were uniquely yours, using metrics or outcomes to quantify impact. Emphasize how your work interfaced with cross-functional partners and how you navigated ambiguity to deliver results.

Pro tip: Use 'I' statements for your contributions but acknowledge team wins—interviewers want to see both ownership and collaboration. Quantify impact wherever possible (e.g., 'improved model accuracy by 15%') to make your contribution memorable.

1. Clarify Your Role

State your official title and the specific scope of your responsibilities within the project, distinguishing your role from others on the team.

2. Highlight Key Contributions

Describe 2-3 concrete actions you took that were critical to the project's success, focusing on technical work and problem-solving.

3. Show Cross-Functional Collaboration

Explain how you worked with other teams (e.g., product, data, engineering) to align goals, share insights, or integrate your work.

4. Demonstrate Adaptability

Mention any ambiguities or changes in direction you faced and how you adjusted your approach to keep the project on track.

5. Quantify Impact

Conclude with measurable outcomes or results that directly resulted from your contributions, such as performance improvements or business metrics.

Key Points to Mention

Your specific role and responsibilities (e.g., 'I was the lead ML engineer responsible for model development').
Concrete technical contributions (e.g., 'I designed and implemented a new feature engineering pipeline').
Cross-functional collaboration (e.g., 'I worked closely with product managers to define success metrics').
Adaptability to ambiguity (e.g., 'When requirements changed, I quickly pivoted to a new modeling approach').
Quantifiable impact (e.g., 'The model increased engagement by 10% and reduced latency by 20%').
Lessons learned or how you grew from the experience (optional but shows self-awareness).

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