Roles at the core of modern AI teams.
A focused view of the AI and Machine Learning roles we recruit for across product, platform and research teams.
Role Categories
Product Facing AI Roles
These roles sit close to product, customers, and decision-making. They require a balance of technical depth, pragmatism, and collaboration with product and engineering teams.
Machine Learning Engineer (Product)
Applied Data Scientist
AI Engineer
ML Software Engineer
Product-focused Research Engineer
Titles vary widely. We focus on scope and responsibility rather than labels.
Platform & Infrastructure Roles
These roles build and maintain the foundations that enable AI teams to operate at scale. Data platforms, ML infrastructure, tooling, and reliability layers that support both research and product teams.
Machine Learning Platform Engineer
Data Platform Engineer
MLOps Engineer
Infrastructure Engineer (AI / ML)
Research Infrastructure Engineer
These hires often sit between research, product, and core engineering functions.
Research & applied science roles
These roles operate across experimentation, modelling, and the translation of research into applied outcomes.
Applied Research Scientist
Research Scientist (Applied)
Research Engineer
Senior Data Scientist (Applied)
Machine Learning Scientist
We distinguish carefully between academic research profiles and roles expected to deliver production-facing impact.
Senior & leadership Roles
We support teams making senior or foundational AI hires where judgement, scope, and long-term impact matter as much as hands-on capability.
Principal Machine Learning Engineer
Staff AI Engineer
Head of Machine Learning
Head of Data Science
AI / ML Technical Lead
These hires often shape teams, standards, and direction long after the initial search concludes.