Sr. Software Engineer- AI/ML, AWS Neuron Distributed Training

Amazon

Seattle, WA

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JOB DETAILS

SALARY

SKILLS

Amazon Elastic Compute Cloud (EC2), Amazon Web Services (AWS), Architectural Design, Artificial Intelligence (AI), Cloud Computing, Code Reviews, Coding Standards, Computer Architecture, Computer Science, Computer Skills, Conferences, Cross-Functional, Customer Support/Service, Data Partitioning, Design Patterns Programming Methodologies, Design Verification, Diversity, Engineer in Training (EIT), Hardware Design, JAX (Java API for XML), Machine Learning, Mentoring, Modeling Languages, Operational Support Systems (OSS), Performance Management, Performance Tuning/Optimization, Preferred Provider Organization (PPO), Programming Languages, Software Design, Software Development, Software Development Lifecycle (SDLC), Software Engineering, Source Code/Configuration Management (SCM), Startup, Team Lead/Manager, Technical Leadership, Testing

LOCATION

Seattle, WA

POSTED

30+ days ago

Description 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-OSS, Quen 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 Pythorch is a must. Distributed training with awareness of strategies like FSDP (Fully-Sharded Data Parallel), PP, Context parallel. Distributed training libraries like torchtitan, torchtune , HF RL , DeepSeek etc are central to this and extending all of this for the Neuron based system is key focussing on enabling large scale training. Experience is post-training strategies like DPO/PPO/HF torch-tune will additional strength and aligns with team success. Key job responsibilities You will lead efforts to build distributed training support into PyTorch, the Neuron compiler, and runtime stacks. You will enable distribute training strategies as well as use them to optimize models to achieve peak performance and maximize efficiency on AWS custom silicon, including Trainium servers. Strong software development skills, the ability to deep dive, work effectively within cross-functional teams, and a solid foundation in Machine Learning are critical for success in this role. 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. 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. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. About AWS Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud. Mentorship & Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. 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. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, 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. If the country/region you're applying in isn't listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $151,300/year in our lowest geographic market up to $261,500/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.

About the Company

Amazon

At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.

Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.

It’s Always Day 1
At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here

Our Leadership Principles
Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles

COMPANY SIZE

10,000 employees or more

INDUSTRY

Other/Not Classified

FOUNDED

1994

WEBSITE

http://Amazon.com/militaryroles