Sr Software Dev Engineer, 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 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.