Full Job Description
Key job responsibilities
In this role you will leverage both your engineering and machine learning background to help develop generative AI for shopping. On a day-to-day basis, you will:
- Design and implementation of a stable and efficient training system for model training and reinforcement learning that scale to various of model sizes and architecture.
- Collaborate with other talented applied scientists and engineers to improve training efficiency and reliability that accelerates innovation.
- Design and implement scalable data infrastructure: that handle Amazon-scale data ingestion, processing, and delivery across different training and evaluation stages;
- Quickly learn and adopt state-of-the-art technologies and algorithms in the field of Generative AI.
A day in the life
On any given day, you may work on:
Design and build end-to-end RL post-training pipelines (rollout 1 reward 1 optimization) at cluster scale
Improve RL training stability (PPO / GRPO / RLOO) by monitoring and tuning key metrics such as reward, KL divergence, and policy stability
Optimize RL post-training efficiency (GPU utilization, batching, sequence packing, async rollouts)
Partner with research scientists to translate new RL algorithms into scalable, production-ready systems
Profile and eliminate bottlenecks across compute, networking, and storage
Build observability systems for training dynamics, system health, and experiment tracking
Collaborate cross-functionally to run experiments, iterate quickly, and unblock research progress
Contribute to system design and long-term technical roadmap
About the team
The SFAI Training Infrastructure team builds a unified platform for large-scale LLM training, supporting the full lifecycle from pretraining to fine-tuning and RL post-training. We focus on solving hard system challenges at the intersection of distributed systems and machine learning, building a platform that is:
Scalable - Efficiently train modern model architectures across large-scale compute environments
Reliable - Enable long-running jobs through fault tolerance, monitoring, and automated recovery
Efficient - Maximize hardware utilization and throughput through system-level optimizations
Simple and Unified - Provide a consistent, config-driven interface across models and workflows
BASIC QUALIFICATIONS
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
PREFERRED QUALIFICATIONS
- Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT
- Knowledge of system performance, memory management, and parallel computing principles
- Experience with CUDA/C++/Kernel development
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually
