Sr Software Dev Engineer, Stores Foundational AI -SFAI

Description

We're building a foundational LLM for Amazon Stores that fuses general world knowledge with Amazon e-commerce domain knowledge to provide new and improved shopping experiences for our customers. We 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
In this role you will leverage your engineering background and expertise to help develop generative AI for shopping. As a Senior Software Development Engineer, you will:
Architect and build scalable ML infrastructure that powers the training and deployment of large language models—directly shaping the future of AI-driven shopping experiences for all Amazon customers
Drive technical innovation by designing experimentation frameworks and tooling that accelerate breakthrough insights, enabling scientists and engineers to iterate faster and smarter
Lead cross-functional initiatives partnering with applied scientists and engineering teams to translate frontier research into production systems that delight customers
Mentor and elevate the team through technical leadership, code reviews, and architectural guidance—raising the bar for engineering excellence across the organization
Own impactful projects end-to-end across diverse technologies—from distributed computing and ML operations to prompt engineering—while navigating ambiguity and making strategic trade-offs that balance innovation with delivery
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
Mentor engineers and contribute to system design and long-term technical roadmap