ROLE MINER

ML engineer jobs

ML engineering is splitting into applied (model serving, MLOps) and research-adjacent (modelling, evals). We surface listings across both tracks.

8 matching roles in cache · refreshed daily

About these roles

The ML engineer title has fractured into two distinct hiring tracks. The applied track is closer to traditional software engineering: building model-serving infrastructure, eval harnesses, retrieval pipelines, LLM-orchestration backends. Strong Python + distributed-systems fundamentals matter more than research credentials here. The research-adjacent track is closer to applied science: fine-tuning, evals, training infra, the boundary between MLE and research scientist. PhDs and publications start mattering at the foundation-model labs but not at most product companies. Comp at senior applied-ML lands $230-350k US base + meaningful equity at AI-native scale-ups; research-adjacent roles at the labs push $300-500k+. London applied-ML senior bands at £130-200k. Most processes include an ML-system-design round in addition to standard coding and design — expect to whiteboard a recommendation system or an LLM eval pipeline end-to-end.

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