Amazon
Mid
Build the tooling that lets 100+ Neuron engineers ship faster on custom AI silicon
SDE I role on Amazon's Annapurna Labs / AWS Neuron team, building profiling, optimization, and resource-management tooling for ML workloads running on Inferentia and Trainium accelerators. Sits at the intersection of Kubernetes, custom silicon, and large-scale ML infrastructure, with real design ownership on a small, senior team building greenfield capabilities.
走进这场面试
免费 · 一场按这个岗位校准的真语音模拟
可能的面试形态
Typical Amazon SDE loop: online assessment/phone screen with coding, followed by an onsite/virtual loop mixing coding, system design, and Leadership Principles behavioral interviews.
这场面试考什么
- Java proficiency plus a secondary language (Go, Python, or TypeScript)
- Kubernetes/Docker/containers ecosystem fundamentals
- Building tooling for profiling, optimization, and resource management of ML workloads
- Software development lifecycle discipline: design, test, build, deploy, CI/CD with Git
- Cross-functional collaboration with hardware, software, and customer-facing teams
- Root-causing software defects and building automation/metrics
常见提问方向
Design a tool to profile or optimize ML workloads on custom accelerators
Explain a container/Kubernetes concept and how you'd apply it to a deployment pipeline
Walk through a coding exercise in Java (or Go/Python/TypeScript)
Describe owning a component end-to-end in a fast-moving, ambiguous environment
How would you instrument and root-cause a production software defect
Amazon Leadership Principles behavioral questions (Ownership, Dive Deep, Bias for Action)
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