The teams we are built to support.
We work with teams who are building and scaling AI, in different ways and at different stages. What they share is the need for hires who can operate in real environments, not just look strong on paper.
Product Teams
We work with product-led teams building AI into customer-facing products, internal tools, and decision-making systems where outcomes matter more than novelty.
Hiring for these teams requires people who can operate across ambiguity, collaborate closely with product and design, and make sensible trade-offs between modelling ambition and delivery reality. Strong theory alone is rarely enough.
We focus on identifying candidates who have shipped, iterated, and owned AI systems in production environments. People who understand not just how models work, but how they are used, monitored, and evolved once they are live.
We support platform and infrastructure teams responsible for the foundations that AI depends on. Data platforms, ML infrastructure, tooling, and reliability layers that enable research and product teams to operate at scale.
These roles demand a different profile. Systems thinking, operational judgement, and an understanding of how downstream teams actually consume platforms are critical. Pure optimisation without empathy for users often creates friction rather than leverage.
Our work here focuses on identifying engineers who can balance robustness with usability, and who understand the long-term consequences of architectural decisions across growing organisations.
Platform & Infrastructure Teams
Research & Applied Science Groups
We work with applied research and science teams operating across experimentation, modelling, and deployment, often in environments where rigour and speed need to coexist.
Hiring in these contexts requires careful distinction. Academic depth, applied research capability, and production-facing data science are not interchangeable, even when titles suggest otherwise.
We take time to understand where a team sits on that spectrum and tailor search accordingly. This leads to hires who can contribute meaningfully, rather than strong CVs that struggle to translate research into real-world impact.
We partner with organisations at different stages of maturity, from scaling teams making their first senior AI hires to established businesses evolving complex, multi-disciplinary AI functions.
Each stage brings different constraints. Early teams may need generalists who can shape direction, while mature organisations often require specialists who can operate within existing systems, governance, and stakeholder structures.
Our approach adapts to these realities. The aim is always to support hires that fit the organisation as it is today, while still being capable of growing with it.
