Building the founding ML robotics team for a humanoid robotics startup.

Brief

General Autonomy engaged us in August 2024 to build their founding ML robot learning team across the UK and US.

They needed hands-on robotics ML scientists who could accelerate research, shape technical direction, and operate in an extremely early-stage environment with almost no established hiring infrastructure.

Challenges

General Autonomy was hiring at a true zero-to-one stage, with fewer than ten people and almost no brand presence in the UK or US. Every candidate interaction required precise framing of the company’s and founder’s mission to generate initial interest.

The talent they needed was exceptionally scarce. ML roboticists with hands-on experience in robot learning, control and manipulation exist in very small numbers, and most are already embedded in top robotics labs or high-profile humanoid startups. Competition for these profiles was intense, with candidates quickly pulled into simultaneous processes.

Cross-border expansion across India, the UK and the US introduced operational friction. Time zone gaps made it difficult to run fast, structured interviews, slowing feedback loops and risking candidate disengagement.

At the same time, the company lacked a formal hiring process and had limited bandwidth to run deep technical assessments. With the founders focused on research and engineering, they needed a partner who could drive the process end-to-end and maintain consistency across regions.

Approach

• We began by embedding directly into General Autonomy’s early team, running weekly syncs, managing pipelines and acting as their primary point of contact across the UK and US.
• We took ownership of the majority of technical interviews ourselves, moving candidates to late-stage assessment to compensate for time zone limitations and maintain momentum through the process.
• We introduced structure by defining screening flows, aligning evaluation criteria and tightening interview sequencing to ensure consistent and timely decision-making.
• We mapped the global ML robotics landscape, covering leading research groups and emerging humanoid robotics companies, and built targeted pipelines of mid-level robot learning talent.
• We refined General Autonomy’s pitch to candidates by highlighting the scientific vision and early technical roadmap in a way that resonated with applied roboticists and researchers.
• We maintained strict SLAs around feedback and scheduling to minimise delays, support fast cross-border hiring cycles and keep candidates engaged throughout evaluations.
• We later supported the founding team to find and secure UK office space.