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57 open Machine Learning Engineer jobs

Real, currently-open Machine Learning Engineer roles synced directly from company career pages. Upload your resume to see which of these match your skills, or browse all open roles. New to the role? Read our Machine Learning Engineer guide.

Hiring snapshot for Machine Learning Engineer

The most common locations right now are United States (5), San Francisco (3), Paris, France (3). 17 of these 57 roles (30%) list a remote option. 38 were posted in the last two weeks. By team, the biggest groups are Engineering (50), Data & AI (7).

Computed from the 57 open listings shown below.

Guide: working as a Machine Learning Engineer

Machine learning engineers take models from experiment to reliable production systems. They build training pipelines, package and serve models behind APIs, monitor accuracy and drift after launch, and help data scientists make research code reproducible, efficient and safe to retrain. Increasingly the role also covers integrating large language models into products.

Core skills employers look for

  • Python and software engineering
  • PyTorch or scikit-learn
  • Model evaluation and validation
  • Feature pipelines, data versioning
  • Model serving behind APIs
  • Docker and cloud deployment
  • Monitoring drift and performance
  • LLM integration and retrieval

How to stand out when you apply

  • Deploy a model, not just train one: wrap it in an API, containerise it, add input validation and logging, and describe how you would roll back a bad version.
  • Show reproducibility with pinned dependencies, tracked experiments, fixed random seeds and a script that retrains the model from raw data in one command.
  • Write up an evaluation beyond a single accuracy figure, covering a baseline, error analysis on slices of the data and the precision versus recall trade-off you chose.
  • If you have built with large language models, show a retrieval or agent project with an evaluation set and notes on latency, cost and the failure cases you found.

What the interviews usually cover

Expect a coding round, ML fundamentals such as bias and variance, regularisation and metric choice, and an ML system design question like building a recommendation or fraud detection pipeline. Interviewers probe data leakage, offline versus online evaluation, serving latency and monitoring. Roles focused on generative AI may add questions on retrieval, prompting and evaluating model outputs.

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