mid Software Engineer ic · Posted Feb 5, 2026
$143,700 – $194,400
USD per year

About this role

Amazon is hiring a mid-level Software Engineer based in Seattle, WA. The posting calls out experience with AWS, PyTorch, LLMs, Machine Learning. Compensation is listed at $143,700–$194,400 per year.

Role
Software Engineer
Function
software engineering
Level
mid
Track
Individual contributor
Employment
Full-time
Location
Seattle, WA
Department
Software Development
Posted
Feb 5, 2026
AI Summary
Optimize distributed training performance on AWS Trainium accelerators for large-scale ML models like Llama and Stable Diffusion. Resolve performance bottlenecks across PyTorch, JAX, compiler, and runtime stack. Requires strong software engineering background with ML systems experience.

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Job description

from Amazon careers

Annapurna Labs designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time ago—even yesterday. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world. AWS Neuron is the complete software stack for the AWS Trainium and Inferentia cloud-scale machine learning accelerators and the Trn3/Trn2/Trn1 and Inf2/Inf1 servers that use them. This role is for a software engineer in the Distributed Training team for AWS Neuron. This role is responsible for development, enablement and performance tuning of a wide variety of ML model families, including massive scale multi-modal large language models like Llama, Qwen, gpt-oss, DeepSeek and beyond, as well as multi-modal generation models such as Stable Diffusion, Flux, WAN, and many more. The Distributed Training team works side by side with chip architects, compiler engineers and runtime engineers to create, build and tune distributed training solutions with AWS Trainium, maximize training throughput, minimize time-to-convergence, and push the boundaries of training efficiency on Trainium. You will identify and resolve performance bottlenecks across the stack, from collective communications and…

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