MLOps and ML Infrastructure Jobs

MLOps and ML infrastructure roles sit between model development and production. The work is training pipelines, feature and data stores, model serving, GPU scheduling, observability and the release process that gets a model from a notebook into a system that runs every day. Titles in this hub include AI platform engineer, ML infrastructure engineer, ML platform engineer and, at the hardware end, GPU and AI infrastructure engineer.

There are about 450 live listings as of September 2026, with around 50 posted in the last week. Slightly more than half are tagged remote. The US accounts for roughly 190 listings, and the UK, India, Germany and France follow with far smaller counts. Employers posting most often include SpaceX, SentinelLabs, Anduril, Nuro, Highmetric and Together AI, which spans aerospace, security, defense, autonomous delivery and AI inference providers.

Seniority is more evenly spread than in most hubs. Mid level and senior roles are almost tied at about 130 each, staff and principal roles number about 100, and lead and manager openings around 65. That staff share, roughly one in five, is high, and it reflects how much of this work involves designing systems rather than operating them. Pay is stated in USD on about 18 percent of listings; for those, the median midpoint is close to $197,500 a year. The rest leave compensation out.

Search this hub with the level filter first, since the gap between a mid level platform engineer and a staff infrastructure architect is wide. The AI infrastructure engineer hub overlaps heavily with this one and is worth a look, as is remote MLOps jobs if location does not matter to you. Kubernetes ML jobs and CUDA engineer jobs cover the narrower tooling ends.