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MLOps engineer jobs

MLOps is the highest-paying ML speciality right now. Training infrastructure, model serving, observability — the plumbing that makes ML work at scale.

11 matching roles in cache · refreshed daily

About these roles

MLOps hiring is dominated by companies that have already crossed the threshold of running ML in production at scale — meaning the pool of paying employers is smaller than for generalist backend or even applied ML, but the comp is unusually high because the skill set is genuinely rare. Strong MLOps candidates combine deep Kubernetes + cloud (especially GPU scheduling, autoscaling, spot management), feature-store and model-registry experience (Feast, Tecton, MLflow, Weights & Biases), and the systems-design fluency to reason about training-pipeline throughput vs cost. The foundation-model labs and the AI-platform tier (Anyscale, Modal, Replicate, Together) drive much of the demand. Comp lands $260-400k US base + significant equity, £150-220k London. Expect interviews to include both classical distributed-systems design and an ML-platform deep dive on something you've actually shipped.

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