FAANG Specific Technology and Culture Questions
Understanding of what makes each FAANG company's technical challenges and culture unique. Google focuses on scale and distributed systems. Amazon emphasizes customer obsession and operational excellence. Meta focuses on mobile and infrastructure. Apple emphasizes hardware-software integration and user experience. Netflix is known for microservices and freedom and responsibility culture. Microsoft has become increasingly cloud-focused with Azure. Understanding each company's technical philosophy helps you source engineers who align with that culture.
EasyTechnical
0 practiced
Explain the engineering tradeoffs between centralizing model training on a large GPU cluster (a Google-style approach) versus pushing more inference and personalization to edge devices (an Apple-style approach). For a visual search application, list three pros and cons of each approach and describe scenarios where a hybrid approach is ideal.
EasyTechnical
0 practiced
List five interview questions you would ask a candidate to assess fit specifically for Google's culture of scale and engineering excellence when hiring an AI engineer. For each question, explain what a strong answer would include and what weaknesses would be concerning.
MediumTechnical
0 practiced
You're leading an AI team that must partner with an SRE team (Google-style) to deploy a distributed training service. How would you structure responsibilities, SLAs/SLOs, runbooks, on-call rotations, and collaboration rituals to reflect engineering excellence and site-reliability practices? Include an example escalation path and handoff protocol.
HardTechnical
0 practiced
You are asked to hire an AI research team that must deliver both publishable research and production-grade systems (a common tension at Google and Meta). Design role definitions, performance evaluation criteria, incentives, and career progression paths that balance open research with production delivery while avoiding perverse incentives (e.g., publishing at the expense of product quality).
HardTechnical
0 practiced
Implement an offline log-analysis tool in Python that helps detect microservice regressions caused by a new ML model version (Netflix-style). Input: JSON lines logs with fields {request_id, timestamp, model_version, latency_ms, error_flag, user_cohort}. The tool should aggregate by model_version and user_cohort, detect statistically significant regressions in latency or error rate (choose an appropriate test), and emit a short actionable alert summary per regression. Focus on correctness and explainability, not performance.
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