About the job
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.
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.
Qualifications
Minimum
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
Preferred
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.