Amazon
Mid
SDE building tooling to profile, optimize, and manage ML workloads on Amazon's custom AI accelerators
This role is on the AWS Neuron team at Annapurna Labs (the silicon group behind Trainium/Inferentia), building developer-facing tooling for profiling, optimization, and resource management of ML workloads on custom accelerators. The interview should probe general-purpose programming strength (Java plus one of Go/Python/TypeScript), OO design and data structures, and exposure to the intersection of Kubernetes, custom silicon, and large-scale ML infrastructure.
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What this interview tests
- Programming fundamentals: OO design, data structures, algorithms
- Proficiency in Java plus Go/Python/TypeScript
- Tooling for profiling, optimization, resource management of ML workloads
- Distributed systems and multi-tiered architecture exposure
- AWS services (EKS, EC2, Lambda, S3, DynamoDB, SQS)
- Compiler toolchains / instruction set architecture exposure (nice-to-have)
Common question themes
Implement a data structure or algorithm and discuss complexity tradeoffs
Design a tool to profile and optimize an ML workload on custom hardware
Explain your Git/CI-CD pipeline experience from an internship or project
How would you diagnose a performance bottleneck on a Linux system
Describe a time you worked with SQL or NoSQL databases in a project
What's your experience (if any) with compiler toolchains or ISAs (CPU/NPU/GPU)
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