Software Development Manager, SageMaker Training

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 hiring a Software Development Manager to lead the team that owns those capabilities.

Model customization is the fastest-moving layer of the machine learning stack. Post-training has moved 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. Agentic training adds environments and tool calls, and moves the bottleneck from one run to the next. Your team turns each new technique into a capability customers can use, without rebuilding the platform every time the research moves.

Underneath the techniques, this stays a hard distributed systems problem. Training jobs run across large accelerator fleets for days at a time, and a single node failure can stall the job. The team works in PyTorch and across frameworks including Verl, FSDP, Megatron and vLLM, and contributes upstream to the open source projects the platform depends on.

As the manager you own the team and its charter. You will hire and grow engineers, set technical direction with your senior engineers and product managers, and decide what the team does and does not build. You own the roadmap and defend its tradeoffs with leadership. You own the service in production, including on-call health, operational metrics, and the requests that come with sitting in the critical path of customer training workloads.

Key job responsibilities

- Own the team's charter and roadmap, and defend its tradeoffs with senior leadership.

- Hire, develop and retain engineers in a specialized field with a scarce talent pool.

- Set technical direction with your senior engineers, applied scientists and product managers.

- Decide what the team builds, and what it does not.

- Turn new post-training techniques into supported customer capabilities on a predictable schedule.

- Own the service in production, including on-call health, availability and operational metrics.

- Improve training throughput and cost per run across large accelerator fleets.

A day in the life

Your morning starts in a design review. The team is working out how to keep an inference engine and a training engine in the same loop for reinforcement learning without leaving accelerators idle, and the answer will shape the future of the platform. You deep dive into possible scenarios, because at this scale a node dies routinely and recovery has to be normal rather than exceptional.

Then a charter call. A new post-training technique is three months out of the research literature and two customers are already asking for it. With your senior engineers and product manager you decide whether it becomes a first-class capability, a recipe on top of what you already have, or a no. That decision is yours to make, and the industry does not wait for it.

You spend an hour in 1:1s, including a career conversation with an engineer who wants to own the parallelism work. After lunch you sit a hiring debrief and make the call. Later you meet the applied scientists to understand what they have validated, and you argue about sequencing.

You close the day writing a page for leadership: what you are not building this half, and why that is the right trade.

About the team

We build the managed services customers use to customize foundation models. A customer brings a task, a dataset, and a way to score a good answer. 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 and the accelerator fleet it runs on.

That makes this a foundational charter. Model customization is how a customer turns a general model into one that is theirs, and every customization experience on SageMaker is built on what we deliver. When we add a technique, it becomes available to every customer on the platform. When we make the training loop faster, every customer's job gets cheaper.

Basic qualifications

- 3+ years of engineering team management experience

- 7+ years of working directly within engineering teams experience

- 5+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience

- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations

- Experience partnering with product or program management teams

- Experience in recruiting, hiring, mentoring/coaching and managing teams of Software Engineers to improve their skills, and make them more effective, product software engineers

Preferred qualifications

- Experience managing teams, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution

- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience with PyTorch, JIT compilation, and AOT tracing

- Experience with deep learning libraries such as PyTorch, TensorFlow, MxNet Research publications in computer vision, deep learning or machine learning at peer-reviewed workshops, conferences or journals

- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience in debugging, profiling, and implementing software engineering best practices in large-scale systems

- Experience with training and deploying machine learning systems to solve large-scale optimizations

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

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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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, CA, Santa Clara - 212,700.00 - 287,700.00 USD annually