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Remote AI Platform Engineer jobs

7 open positions in the United States, newest first.

AI platform engineers build and operate the shared infrastructure that lets other teams train, deploy and run machine learning and large language models. They provide the GPU compute, model serving, pipelines and tooling so that data scientists and product engineers do not each have to build their own. They are responsible for the platform's reliability, cost and security.

About the AI Platform Engineer role

What the work looks like

  • Run GPU clusters on Kubernetes, managing scheduling, quotas and autoscaling for training and inference workloads.
  • Deploy and tune model serving systems, watching latency, throughput and the cost of each request.
  • Build internal tooling for experiment tracking, model registries, evaluation runs and prompt or model versioning.
  • Operate gateways to hosted model APIs, handling rate limits, fallbacks, logging and access control.
  • Work with ML researchers and product engineers to move a prototype model into a monitored production service.

What employers ask for

  • Kubernetes and cloud infrastructure with GPUs
  • Python plus infrastructure as code
  • Model serving frameworks such as vLLM or Triton
  • MLOps tooling like MLflow or Kubeflow
  • Observability and cost monitoring
  • Familiarity with PyTorch and LLM APIs

Doing this job remotely

The platform lives in cloud accounts and data centers reached over the network, so the work is location-independent. Production inference services need on-call coverage, and GPU capacity problems do not wait for business hours. Access to proprietary models and training data usually brings tight security controls on devices and credentials.

Where it comes from and where it leads

Engineers arrive from DevOps, site reliability, backend or data engineering, or from ML engineering with an infrastructure bent. The path leads to senior and staff platform roles, ML infrastructure architect or engineering management.