Backed by General Catalyst.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
More about this role
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...
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