Level

AmazonPosted 3mo ago

Software Dev Engineer II, Stores Foundational AI -SFAI

Software Dev Engineer II, Stores Foundational AI -SFAI at Amazon scores 99 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

US, WA, Seattlemidfull-time$144k-$194k

AI in this role

vllmpytorchjax
fine-tuning
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

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

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Fine TuningvLLMPyTorchJax

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. Walk me through how you've used vLLM in your day-to-day work.
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. What's a project where you used Jax hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: Fine Tuning, vLLM, PyTorch, and Jax. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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