Amazon
ML Infrastructure Engineer - Distributed Training, AWS Neuron, Annapurna Labs
Amazon, Seattle, Washington, us, 98127
By applying to this position, your application will be considered for all locations we hire for in the United States.
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 (Trn1/Trn2) and Inferentia (Inf1/Inf2) our cloud-scale Machine Learning accelerators. This role is for a Senior Machine Learning Engineer in the Distribute Training team for AWS Neuron, responsible for development, enablement and performance tuning of a wide variety of ML model families, including massive-scale Large Language Models (LLM) such as GPT and Llama, as well as Stable Diffusion, Vision Transformers (ViT) and many more.
The ML Distributed Training team works side by side with chip architects, compiler engineers and runtime engineers to create, build and tune distributed training solutions with Trainium instances. Experience with training these large models using Python is a must. FSDP (Fully-Sharded Data Parallel), Deepspeed, Nemo and other distributed training libraries are central to this and extending all of this for the Neuron based system is key.
Key job responsibilities
You'll help develop and improve distributed training capabilities in popular machine learning frameworks (PyTorch and JAX) using AWS's specialized AI hardware. Working with our compiler and runtime teams, you'll learn how to optimize ML models to run efficiently on AWS's custom AI chips (Trainium and Inferentia). This is a great opportunity to bridge the gap between ML frameworks and hardware acceleration, while building strong foundations in distributed systems.
We're looking for someone with solid programming skills, enthusiasm for learning complex systems, and basic understanding of machine learning concepts. This role offers excellent growth opportunities in the rapidly evolving field of ML infrastructure.
About the team
Annapurna Labs was a startup company acquired by AWS in 2015, and is now fully integrated. If AWS is an infrastructure company, then think Annapurna Labs as the infrastructure provider of AWS. Our org covers multiple disciplines including silicon engineering, hardware design and verification, software, and operations. AWS Nitro, ENA, EFA, Graviton and F1 EC2 Instances, AWS Neuron, Inferentia and Trainium ML Accelerators, and in storage with scalable NVMe, are some of the products we have delivered, over the last few years.
BASIC QUALIFICATIONS
- To qualify, applicants should have earned (or will earn) a Bachelors or Masters degree between December 2022 and September 2025. - Working knowledge of C++ and Python - Experience with ML frameworks, particularly PyTorch and/or JAX - Understanding of parallel computing concepts and CUDA programming
PREFERRED QUALIFICATIONS
- Open source contributions to ML frameworks or tools - Experience optimizing ML workloads for performance - Direct experience with PyTorch internals or CUDA optimization - Hands-on experience with LLM infrastructure tools (e.g., vLLM, TensorRT)
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information.
Posted:
March 26, 2025 (Updated 16 minutes ago) Posted:
January 23, 2025 (Updated about 1 hour ago) Posted:
January 17, 2025 (Updated about 17 hours ago) Posted:
March 4, 2025 (Updated about 17 hours ago) Posted:
March 12, 2025 (Updated about 17 hours ago)
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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 (Trn1/Trn2) and Inferentia (Inf1/Inf2) our cloud-scale Machine Learning accelerators. This role is for a Senior Machine Learning Engineer in the Distribute Training team for AWS Neuron, responsible for development, enablement and performance tuning of a wide variety of ML model families, including massive-scale Large Language Models (LLM) such as GPT and Llama, as well as Stable Diffusion, Vision Transformers (ViT) and many more.
The ML Distributed Training team works side by side with chip architects, compiler engineers and runtime engineers to create, build and tune distributed training solutions with Trainium instances. Experience with training these large models using Python is a must. FSDP (Fully-Sharded Data Parallel), Deepspeed, Nemo and other distributed training libraries are central to this and extending all of this for the Neuron based system is key.
Key job responsibilities
You'll help develop and improve distributed training capabilities in popular machine learning frameworks (PyTorch and JAX) using AWS's specialized AI hardware. Working with our compiler and runtime teams, you'll learn how to optimize ML models to run efficiently on AWS's custom AI chips (Trainium and Inferentia). This is a great opportunity to bridge the gap between ML frameworks and hardware acceleration, while building strong foundations in distributed systems.
We're looking for someone with solid programming skills, enthusiasm for learning complex systems, and basic understanding of machine learning concepts. This role offers excellent growth opportunities in the rapidly evolving field of ML infrastructure.
About the team
Annapurna Labs was a startup company acquired by AWS in 2015, and is now fully integrated. If AWS is an infrastructure company, then think Annapurna Labs as the infrastructure provider of AWS. Our org covers multiple disciplines including silicon engineering, hardware design and verification, software, and operations. AWS Nitro, ENA, EFA, Graviton and F1 EC2 Instances, AWS Neuron, Inferentia and Trainium ML Accelerators, and in storage with scalable NVMe, are some of the products we have delivered, over the last few years.
BASIC QUALIFICATIONS
- To qualify, applicants should have earned (or will earn) a Bachelors or Masters degree between December 2022 and September 2025. - Working knowledge of C++ and Python - Experience with ML frameworks, particularly PyTorch and/or JAX - Understanding of parallel computing concepts and CUDA programming
PREFERRED QUALIFICATIONS
- Open source contributions to ML frameworks or tools - Experience optimizing ML workloads for performance - Direct experience with PyTorch internals or CUDA optimization - Hands-on experience with LLM infrastructure tools (e.g., vLLM, TensorRT)
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information.
Posted:
March 26, 2025 (Updated 16 minutes ago) Posted:
January 23, 2025 (Updated about 1 hour ago) Posted:
January 17, 2025 (Updated about 17 hours ago) Posted:
March 4, 2025 (Updated about 17 hours ago) Posted:
March 12, 2025 (Updated about 17 hours ago)
#J-18808-Ljbffr