Delivering two key hires for a fast-growing BioAI startup.

Brief

GenomeKey partnered with Cubiq to secure critical technical hires as they scaled their AI-driven genomics platform.

The initial requirement was for a Senior Data Engineer to own ML data pipelines, training infrastructure, and production data operations as the computer science team expanded.

GenomeKey then released a second requirement for a Machine Learning Engineer to act as the sole ML lead on a bacterial genome anomaly detection project.

Cubiq partnered directly with the CTO, founding team, and engineering leadership across both hires to define requirements and accelerate the hiring process.

Challenges

GenomeKey were competing for senior and highly specialised talent against well-funded AI labs and techbios.

Both roles required hybrid skill sets, combining deep engineering capability with domain-specific genomics and machine learning research experience, which is a rare combination skillset in the UK market.

The ML Engineer role was particularly niche, with very few candidates working directly on bacterial genome anomaly detection or closely related BioAI research.

Previous agency efforts had failed to surface suitable candidates within GenomeKey’s technical constraints.

Approach

• We partnered directly with the founding team to define the technical profile required across both hires, outlining the machine learning infrastructure, data engineering and genomics research expertise required.
• For the Senior Data Engineer, we targeted those with experience building ML data pipelines and production infrastructure in research-heavy environments, focusing on candidates capable of scaling data systems to support genomics AI workloads.
• The ML Engineer search required a different strategy due to its niche focus on bacterial genome anomaly detection. We expanded sourcing beyond conventional recruitment channels, identifying candidates through scientific journals and publication mapping across leading AI labs and research groups.
• This research-led sourcing approach surfaced candidates with directly relevant domain expertise, with the eventual hire for the ML position sourced from a publication in Nature.
• Throughout the engagement we maintained a tightly managed process with the CTO and engineering leadership, ensuring fast feedback, early compensation alignment and minimal interview friction.
• This focused delivery model prioritised a small number of highly-qualified candidates which enabled GenomeKey to move from briefing to successful hire quickly across both roles.