ML Platform jobs

ML platform teams build the paved road inside a company: feature stores, training pipelines, model registries, and deployment paths that let a product team ship a model without filing tickets to three other teams. It's infrastructure with a product sensibility — your users sit two desks away and will absolutely route around you if the road is worse than the jungle.

Kubernetes, Ray, and MLflow or SageMaker form the base layer, and lately every platform team is bolting on an LLM lane: prompt registries, eval integration, GPU quotas. Success is measured in other teams' velocity, which suits engineers who prefer leverage to spotlight. Mid-size and large companies hire this steadily, and remote arrangements are common.