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)

How candidates describe it

Real Software Development Engineer I interview stories — retold from candidates' public write-ups, with sources.

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