Software Dev Engineer II, Stores Foundational AI -SFAI
We’re working to improve shopping on Amazon using the capabilities of large language models (LLM), and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists and engineers to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!
Key job responsibilities
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 → reward → 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
Key job responsibilities
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 → reward → 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