Member of Technical Staff - Reinforcement Learning (Infrastructure), AGI Autonomy

Amazon•San Francisco, CA

72d•$255,000 - $345,000

About The Position

The Amazon AGI SF Lab is focused on developing new foundational capabilities for enabling useful AI agents that can take actions in the digital and physical worlds. We’re enabling practical AI that can actually do things for us and make our customers more productive, empowered, and fulfilled. The lab is designed to empower AI researchers and engineers to make major breakthroughs with speed and focus toward this goal. Our philosophy combines the agility of a startup with the resources of Amazon. By keeping the team lean, we’re able to maximize the amount of compute per person. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. In this role, you will work closely with research teams to design, build, and maintain systems for training and evaluating state-of-the-art agent models. Our team works inside the Amazon AGI SF Lab, an environment designed to empower AI researchers and engineers to work with speed and focus. Our philosophy combines the agility of a startup with the resources of Amazon.

Requirements

PhD, or Master's degree and 3+ years of applied research experience
Experience with programming languages such as Python, Java, C++
Experience with neural deep learning methods and machine learning
Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience troubleshooting and debugging technical systems

Nice To Haves

PhD, or a Master's degree and experience with various machine learning techniques and parameters that affect their performance
Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Experience with distributed system, Megatron, vLLM, Ray, and working with GPUs.
- Experience with patents or publications at top-tier peer-reviewed conferences or journals.

Responsibilities

Develop training infrastructure to ensure large-scale reinforcement learning on LLMs runs highly efficient and robust.
Work across the entire technology stack, including low level ML system, job orchestration and data management.
Analyze, troubleshoot and profiling complex ML systems, identify and address performance bottlenecks.
Work closely with researchers, conduct MLSys research to create new techniques, infrastructure, and tooling around emerging research capabilities.