Role Team and Company Understanding Questions

Covers researching and demonstrating practical knowledge of the company the hiring team and the specific role. Candidates should be able to describe team mission and composition reporting relationships typical day to day responsibilities success metrics and short term priorities. This topic includes preparing substantive questions about onboarding expectations the first ninety days common technical and product challenges and how the role contributes to company objectives. Interviewers evaluate preparedness the candidate's ability to map their skills to concrete team needs and to propose realistic early contributions and measurable goals.

MediumTechnical

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Create three concise KPIs you would share with product managers to demonstrate the impact of ML work on product outcomes. For each KPI, specify how it is computed, its cadence, owners, and how it ties to business goals.

HardTechnical

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The team’s core product requires labeled examples that are expensive to collect. Describe a prioritized plan of techniques (e.g., active learning, weak supervision, transfer learning, synthetic data) you would evaluate in the first quarter to reduce labeling cost while maintaining model performance. Include evaluation criteria and trade-offs.

MediumTechnical

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Propose three realistic early contributions you could make in the first 60 days that address typical technical or product gaps for an ML team (e.g., reducing model latency, improving feature reliability, establishing A/B testing frameworks). For each contribution, state acceptance criteria and how you'd measure success within 60 days.

MediumTechnical

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Explain how you would prioritize competing requests from product, research, and platform teams during your first quarter as an ML Engineer. Describe the framework or criteria you would use (e.g., impact/effort matrix, risk/reward, alignment to roadmap) and give a short example prioritization of three hypothetical tasks.

MediumTechnical

0 practiced

Draft a concrete 30/60/90-day plan for shipping an MVP model that demonstrates a business hypothesis (e.g., improve recommendation CTR). Include data acquisition steps, baseline model, evaluation criteria, experiment plan, and a roll-out strategy that minimizes user risk.

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