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Remote Machine Learning Engineer jobs

101 open positions in the United States, newest first.

Machine learning engineers build and run the systems that train models and serve their predictions in production. They turn a prototype from a notebook into a reliable service, owning data pipelines, training jobs, deployment and monitoring. The job sits closer to software engineering than to research: the measure is a model that works at scale, not a paper.

About the Machine Learning Engineer role

What the work looks like

  • Build training pipelines in Python with PyTorch or TensorFlow and keep the runs reproducible and versioned.
  • Engineer features from raw event data and maintain the feature pipelines that feed both training and serving.
  • Deploy models behind APIs or as batch jobs, and tune latency, throughput and compute cost.
  • Monitor live models for data drift and degraded accuracy, and retrain or roll back when quality drops.
  • Work with data scientists to turn experimental models into tested, maintainable production code.

What employers ask for

  • Python with PyTorch or TensorFlow
  • Model deployment and serving infrastructure
  • SQL and data pipelines with Spark or Airflow
  • Docker, Kubernetes and a major cloud platform
  • Experiment tracking with MLflow or similar
  • Solid grounding in statistics and model evaluation

Doing this job remotely

The work is done on cloud infrastructure and in code review, so location matters little once access to data and compute is arranged. Constraints come from data governance: some employers restrict where sensitive training data may be accessed. Teams that run models in production usually share an on-call rotation, which ties working hours to the team's time zones.

Where it comes from and where it leads

Most arrive from software engineering or data science, often with a graduate degree in a quantitative field, though strong engineers also move in without one. From here people go on to senior and staff engineering roles, ML platform or infrastructure teams, or engineering management.