AmazonPosted 1d ago

Sr Software Dev Engineer, SageMaker Training

Sr Software Dev Engineer, SageMaker Training at Amazon scores 87 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, Bellevueseniorfull-time$168k-$227k

AI in this role

sagemaker

fine-tuning

Interested in building the distributed systems that let customers customize foundation models at scale? The SageMaker Training and Model Customization team builds the services customers use to fine-tune and post-train models against their own accuracy, reliability, and cost targets. We are looking for a Sr Software Development Engineer to build these capabilities.

Model customization is the fastest-moving layer of the machine learning stack. Post-training has gone from supervised fine-tuning to reinforcement learning against verifiable rewards, and from single-turn tasks to agentic training where a model learns by acting in an environment over long trajectories. Each shift changes the shape of the workload: reinforcement learning puts an inference engine inside the training loop, and agentic training adds environments and tool calls that move the bottleneck from run to run. You turn each new technique into a capability customers can use, without rebuilding the platform every time the research moves. Underneath, it stays a hard distributed systems problem across large accelerator fleets where a single node failure can halt progression.

As a Senior SDE you own your team's architectural direction, including influencing decisions in systems you depend on but don't control. You lead the team's software development alongside peers on related teams, and you are responsible for the quality of what it ships. You take on problems where the customer case is defined but the technology strategy is not, balancing speed of delivery against the foundation for the future and advocating for the right solution. You are a key influencer in the team's strategy and goals, you drive adoption of engineering best practices, and you coach and mentor other engineers.

Key job responsibilities
- Design, develop, and operate the distributed services that run large-scale training and reinforcement learning workloads
- Build the scheduling, capacity, and fleet-health systems that place customer jobs onto large GPU clusters and keep them running through hardware failure
- Build the infrastructure that hosts and scales the engines, including multi-tenant GPU sharing across customers
- Partner with Science, Product, and partner service teams to translate evolving model and customer requirements into scalable technical solutions
- Maintain a high operational excellence bar for services in the critical path of customer training workloads

About the team
We build the managed services customers use to customize foundation models. A customer brings a task, a dataset, and a definition of what a good answer is worth, and our services run the post-training loop on their behalf. We own that entire path, from the customer-facing API down to the training-session infrastructure.

Basic qualifications

- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team

Preferred qualifications

- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent

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, BELLEVUE - 168,100.00 - 227,400.00 USD annually

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Skills and AI tools this role asks for

Fine TuningSagemaker

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Walk me through how you've used Sagemaker in your day-to-day work.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

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List these exact terms on your resume: Fine Tuning and Sagemaker. 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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