Own the ML lifecycle for selected problem spaces: data pipelines, training, evaluation, deployment, and monitoring — with a bias to models that change a business metric.
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Requirements
01Four or more years ML engineering or applied ML in production
02Strong Python, PyTorch or TensorFlow, and experiment tracking discipline
03Experience deploying models behind APIs or batch jobs
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Nice to have
+Computer vision or time-series experience
+Feature stores and MLOps tooling
Hiring
What happens next
01
We read it
A person reads every application. No keyword filter.
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Reply
Within 5 working days if there is a fit.
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Conversation
90 minutes with the people you would work with.
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Paid trial
A real, scoped problem. Paid at contract rate.
Application
Send your work. Skip the theatre.
Resume, a short note, and anything that shows how you think. Specifics beat adjectives.
Show something you built, not something you managed.
Scale, constraints, and trade-offs land better than adjectives.