Role and Team Understanding Questions

Understand and articulate what a role requires in the context of the team's real world operations. This includes the team structure and reporting lines, typical day to day responsibilities, how the role contributes to product goals, key success metrics and service level agreements, current team challenges and technical or process debt, tooling and workflows, collaboration patterns with product, design, sales, support and engineering, expectations for mentoring or ownership, test and quality strategies where relevant, and what success looks like in the first six to twelve months. Candidates should be prepared to ask informed, practical clarifying questions about team priorities, measurement, handoffs, reporting rhythms, and immediate problems the role will address.

HardTechnical

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Build a 2-3 year career growth plan for an AI Engineer aiming to reach Staff level. Include technical and soft skills to develop, project types and ownership opportunities to seek, visibility activities (papers, talks, cross-team projects), mentoring responsibilities, and measurable milestones for each 6-month period.

HardSystem Design

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Design an SLA-backed low-latency serving architecture for fraud detection requiring a maximum 50ms response time for 99.99% of requests. Describe warm pools or pre-warmed instances, model sharding and routing, batching trade-offs, autoscaling policies, fallback strategies for overload, and how you'd validate the 99.99th percentile in testing.

MediumSystem Design

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Design a 6-month roadmap to integrate a pre-trained large language model (LLM) into an existing product feature. Your plan should cover data collection and labeling needs, fine-tuning vs adapter approaches, safety and content filtering, latency and cost constraints, evaluation metrics, A/B rollout strategy, and team responsibilities. Assume a team of 5, limited budget, and 50k daily active users.

MediumTechnical

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How do you balance short-term product requests that require quick prototypes against long-term investments in robust model CI/CD and infrastructure? Propose a prioritization framework (including criteria, decision process, and examples) you would use when stakeholders disagree.

EasyTechnical

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List and briefly explain the essential tooling and workflows an AI Engineer should be proficient with in a modern production ML stack. Cover areas for: training (frameworks/hardware), experiment tracking, model versioning and packaging, CI/CD for models, deployment/orchestration, monitoring and alerting, and data pipelines. Give concrete examples of technologies and why you'd choose them.

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