Hiring in AI & Machine Learning

• Research

  Research scientists · Applied research scientists · Research engineers · Heads of research

• Applied ML engineering

  ML engineers · Senior ML engineers · Staff ML engineers · ML tech leads

• MLOps & platform

  MLOps engineers · ML platform engineers · Data & ML platform leads

• GenAI & LLM

  LLM engineers · Applied AI engineers · AI product engineers · Prompt engineers

• AI product & leadership

  AI product managers · Heads of AI · VPs of ML · Chief AI officers

• Research vs production drift

  Publications and Kaggle badges do not equal production ML. Rubrics score each track on the outcomes it actually owned — inference latency, training pipelines, evaluation harnesses or published research.

• Frontier vs applied

  Frontier-model research and applied LLM engineering demand different evidence. We capture which layer of the stack the candidate lives at.

• Evaluation rigour

  Model quality is only credible with evaluation. We look for named benchmarks, offline test sets, A/B design and monitoring — not vibes.

• Compute and cost fluency

  Serving GPUs at production scale is a distinct skill. Rubrics capture training-cost, inference-cost and quantisation experience explicitly.

Every candidate is scored against the role's rubric, and every score points back to a specific line in the CV. For ai & machine learning, these are the signals that carry weight:

• Track-specific rubric per role
• Model-lifecycle evidence captured
• LLM/GenAI specialisation surfaced

1. A ranked set of candidates, each with a score and the evidence behind it.
2. Eligibility checks resolved before the candidate reaches you — right to work, credentials, location model.
3. Salary expectation against your band, stated plainly, before you invest interview time.
4. One decision per candidate: advance, hold or decline, reversible for a short window.

Candidates stay invisible to you until we've reviewed them for your specific role, and contact details are released as a separate step.