Technical Priorities and Challenges Questions
Identify the team's current technical priorities, pain points, and technical roadmap including architecture, technical debt, platform and tooling constraints, and business intelligence or data infrastructure considerations. Candidates should be able to discuss the current data stack and workflows, trade offs between short term fixes and longer term redesigns, success criteria for technical initiatives in the first 90 days and first year, and how their technical experience and decisions would address the team constraints while aligning with product goals.
HardSystem Design
68 practiced
Design a distributed rate-limiter and backpressure mechanism for an online inference service expected to handle 100k RPS at peak. Include token-bucket or leaky-bucket choices, local vs global enforcement, graceful degradation strategies, and how to avoid cascading failures downstream.
EasyTechnical
84 practiced
You're joining a mid-size AI team as an AI Engineer. In your first week you must surface the team's top technical priorities across model serving, data workflows, infrastructure constraints, platform/tooling limits, and technical debt. Produce a prioritized list (top 6) and explain why each item belongs in the top priorities. Also list the 6 key questions you would ask engineering/product/stakeholder owners to validate assumptions.
HardTechnical
66 practiced
You're leading an AI platform overhaul. Define clear, measurable success criteria for the first 90 days and for the first year that align with product goals, engineering velocity, and operational reliability. Provide at least 5 success metrics per horizon and explain how you'd collect them.
MediumSystem Design
64 practiced
Design an inference API for ML models that supports multiple versions, backward compatibility, canary traffic, and metadata (model-id, version, schema). Include endpoints, request/response shape examples (JSON), and a deprecation strategy for old versions.
EasyTechnical
128 practiced
List and briefly explain the core components of a production model-serving architecture for real-time inference (components such as API gateway, model router, model container, feature cache, telemetry, etc.). For each component state one key constraint (latency, cost, throughput, consistency) that often drives design choices.
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