Job description
AWS Neuron is a software stack for the Annapurna Inferentia and Trainium machine
learning accelerators hosted inside AWS EC2 Trn1/2 and Inf1 servers.
As the Principal Engineer for the Neuron Distributed Training team, you will be responsible for working hands-on with a strong team of engineers to help design and optimize ML on Neuron devices. Specifically focus on bringing up a coherent solution across the stack to increase the training resiliency for ultra clusters with thousands of nodes. You will Scale and Optimize the application stack for LLMs that leverage multi-modal modes of input/output-generation such as Text, Vision, Video, Audio etc. You will be responsible for the full development life cycle of providing Distributed Training support for multi-modal transformer models such as MM-Llama3.2, DiT/Pixart, CLIP etc. You will develop scalability features and performance optimizations in the Neuron ML Framework components to enable them make Trainium devices as the first-class citizens for ML Acceleration. Lead the way to ensure support for key ML functionality in a combined chip / software platform. Ensure the right thing is being built and delivered to customers
A successful candidate will have an established background in Scaling and Stabilizing Machine Learning Distributed Training components along-with a strong technical ability to work/deliver on a vertically integrated system stack that consists of a combinatorial matrix of hardware, frameworks, and workflows. Deep expertise in scaling model training across thousands of nodes a must along-with direct customer-facing experience and a strong motivation to achieve results.
Key job responsibilities
This role will help lead the efforts building distributed training large cluster stability support into Pytorch, Jax 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 TRN2+ servers. Strong software development and ML knowledge are both critical to this role.
Additional details for internal candidates
This role needs ML/DL work experience, with focus on GenAI and LLMs.
Basic qualifications
- Knowledge of object-oriented design, data structures, and algorithms
- Experience (non-internship) in professional software development
Preferred qualifications
- Experience designing and building large-scale systems in a multi-tiered, distributed environment (Service Oriented Architecture)
- Experience in Distributed Training on thousands of nodes.
BASIC QUALIFICATIONS
- 3+ years of engineering team management experience
- 7+ years of working directly within engineering teams experience
- 3+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience
- 8+ years of leading the definition and development of multi tier web services experience
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations
- Experience partnering with product or program management teams
PREFERRED QUALIFICATIONS
- Experience in communicating with users, other technical teams, and senior leadership to collect requirements, describe software product features, technical designs, and product strategy
- Experience in recruiting, hiring, mentoring/coaching and managing teams of Software Engineers to improve their skills, and make them more effective, product software engineers
USA, CA, Cupertino - 212,700.00 - 287,700.00 USD annually
