Software Engineer - AI/ML, AWS Neuron Distributed Training

at  Annapurna Labs US Inc

• 3+ years of building complex software systems experience
• Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
• Experience optimizing machine learning workloads
  Do you love decomposing problems to develop products that impact millions of people around the world? Would you enjoy identifying, defining, and building software solutions that revolutionize how businesses operate?
  The Annapurna Labs team at Amazon Web Services (AWS) is looking for a Software Development Engineer II to build, deliver, and maintain complex products that delight our customers and raise our performance bar. You’ll design fault-tolerant systems that run at massive scale as we continue to innovate best-in-class services and applications in the AWS Cloud.
  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.
  AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machine
  learning accelerators and the Trn1 and Inf1 servers that use them. This role is for a senior software engineer in the Machine Learning Applications (ML Apps) 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 large language models like GPT2, GPT3 and beyond, as well as stable diffusion, Vision Transformers 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 Trn1. Experience training these large models using Python is a must. FSDP, Deepspeed and other distributed training libraries are central to this and extending all of this for the Neuron based system is key.
  Key job responsibilities
  This role will help lead the efforts building distributed training support into Pytorch, Tensorflow using XLA and the Neuron compiler and runtime stacks. This role will help tune these models to ensure highest performance and maximize the efficiency of them running on the customer AWS Trainium and Inferentia silicon and the TRn1 , Inf1 servers. Strong software development and ML knowledge are both critical to this role.
  About the team
  About Us
  Inclusive Team Culture
  Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
  Work/Life Balance
  Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.
  Mentorship & Career Growth

Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.

• Master’s degree in computer science or equivalent
• Experience with writing high performance CUDA kernels, internals of Megatron/FSDP/Deepspeed/GSPMD
• Experience with building roofline models to improve performance
• Experience training large models at scale

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. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,300/year in our lowest geographic market up to $223,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site

• Master’s degree in computer science or equivalent
• Experience with writing high performance CUDA kernels, internals of Megatron/FSDP/Deepspeed/GSPMD
• Experience with building roofline models to improve performance
• Experience training large models at scal