Sr. Software Engineer- AI/ML, AWS Neuron Distributed Training
About the role
Annapurna Labs designs silicon and software that accelerates innovation. Our custom chips, accelerators, and software stacks enable us to tackle unprecedented technical challenges and deliver solutions that help customers change the world. AWS Neuron is the complete software stack powering AWS Trainium (Trn2/Trn3), our cloud scale Machine Learning accelerators and we are seeking a Senior Software Engineer to join our ML Distributed Training team. In this role, you will be responsible for the development, enablement, and performance optimization of large scale ML model training across diverse model families. This includes massive scale pre-training and post-training of LLMs with Dense and Mixture-of-Experts architectures, Multimodal models that are transformer and diffusion based, and Reinforcement Learning workloads. You will work at the intersection of ML research and high performance systems, collaborating closely with chip architects, compiler engineers, runtime engineers and AWS solution architects to deliver cost-effective, performant machine learning solutions on AWS Trainium based systems.
Responsibilities
- Design, implement and optimize distributed training solutions for large scale ML models running on Trainium instances
- Extend and optimize popular distributed training frameworks including FSDP (Fully-Sharded Data Parallel), torchtitan and Hugging Face libraries for the Neuron ecosystem
- Develop and optimize mixed-precision and low-precision training techniques working with BF16, FP8, and emerging numerical formats to maximize training throughput while maintaining model accuracy and convergence quality
- Implement precision aware training strategies, loss scaling techniques, and careful gradient management to ensure training stability across reduced precision formats
- Profile, analyze, and tune end-to-end training pipelines to achieve optimal performance on Trainium hardware
- Partner with hardware, compiler, and runtime teams to influence system design and unlock new capabilities
- Work directly with AWS solution architects and customers to deploy and optimize training workloads at scale
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
- Bachelor's degree in computer science or equivalent
- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Experience as a mentor, tech lead or leading an engineering team
- Experience in machine learning, large scale training with LLMs and expertise in Pytorch
Preferred qualifications
- Master's degree in computer science or equivalent
- Experience in computer architecture
- Previous software engineering expertise with Pytorch/Jax/Tensorflow, Distributed libraries and Frameworks, End-to-end Model Training
Pay
- Base salary range: $193,300.00 - $261,500.00 USD annually (USA, CA, Cupertino)
- Sign-on payments and restricted stock units (RSUs) included
- Final compensation determined based on factors including experience, qualifications, and location
Benefits
- Health insurance (medical, dental, vision, prescription)
- Basic Life & AD&D insurance and option for Supplemental life plans
- EAP, Mental Health Support, Medical Advice Line
- Flexible Spending Accounts
- Adoption and Surrogacy Reimbursement coverage
- 401(k) matching
- Paid time off
- Parental leave