Behavioral: Walk Through Two Recent ML Projects
Context: Technical screen for a Machine Learning Engineer. Focus on technical depth, measurable business/user impact, and leadership.
For each of two projects:
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Problem and context
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What problem did you solve and why did it matter?
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Constraints (latency, memory, privacy, reliability, regulatory, etc.).
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Your role and team
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Your responsibilities (ownership, decisions, leadership).
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Team composition and how you coordinated.
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Architecture and approach
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System/data/model architecture; key components and interfaces.
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Training/inference pipeline; tools and infra.
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Key trade-offs and decisions
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What options you considered and why you chose one.
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Implications on accuracy, cost, latency, maintainability.
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Timelines and execution
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Milestones, phases, and how you de-risked.
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Measurable outcomes
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Metrics and deltas (offline and online), scale of impact.
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Hardest challenge, resolution, and retrospective
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Root cause, how you resolved it, what you’d do differently.
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Collaboration and quality under pressure
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Cross-functional partners (PM, design, infra, privacy, QA, SRE, etc.).
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How you ensured quality under tight deadlines (validation, rollouts, guardrails).