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AI4Science

Machine learning applied to biology, chemistry, materials and climate: AI aimed at the hardest problems in the physical and life sciences. Part of our Machine Learning & Generative AI discipline, we work with researchers and engineers building AI4Science systems from early-stage labs through to production at scale.

AI4Science jobs

Hiring in AI4Science isn't like other technical hiring.

AI4Science hiring means finding people genuinely fluent in both machine learning and a specific scientific domain, whether that's protein structure, materials chemistry or climate modelling, and that dual fluency is rare. A strong ML researcher without domain grounding, or a strong domain scientist without ML depth, is usually not enough on its own.

At Enigma, we track researchers publishing at the intersection of ML and the sciences, so when a search opens up, we're not starting from a keyword search.

NeurIPS
ICML
ICLR
AI4Science Workshops

Many of the teams we support here are also hiring across ML Infrastructure and Computer Vision, so we're used to building the fuller picture of a team, not just filling a single seat.

Start a Search

What a Good Partnership Looks Like

We work best as a close, informed extension of your team, not a CV-forwarding service.

Define the Brief

Align on what "great" actually looks like, whether that's a first AI4Science hire or a computational lead spanning biology or materials.

Map the Market

Understand who's active in AI4Science right now, across biotech, climate and materials labs building applied machine learning.

Focused Search

Target the right people, not more people, drawing on relationships built across NeurIPS, ICML and domain-specific science venues.

Guide the Process

Keep momentum, clarity and alignment from first call through to offer stage.

Deliver & Refine

Secure the hire, then refine the approach for next time so every search gets sharper.

Trusted across the ecosystem

Trusted across the Climate AI ecosystem Trusted across the AI4Science ecosystem Trusted across the Computational Biology ecosystem

FAQs

It depends on the role. Some hires need deep domain expertise, computational biology, chemistry or climate science, with applied ML skills layered on top. Others need strong ML researchers who can partner closely with domain scientists rather than replace them. We help you work out which shape the role actually needs before we start sourcing.

The underlying ML methods often transfer, but domain intuition doesn't always. We probe specifically for how a candidate's prior domain knowledge would map onto your specific problem, rather than assuming ML skill alone is transferable.

No, though it's true many AI4Science teams are capital-intensive given the compute and lab-integration costs involved. We've placed for pre-seed teams as well as later-stage companies, and the hiring approach differs meaningfully between the two.

Yes, and often this is exactly right for AI4Science, where academic labs are frequently at the frontier of the science. We help calibrate what a first industry move looks like for both the candidate and the hiring team.

Meaningfully. Imaging-heavy science, like microscopy or materials characterisation, often needs computer vision expertise, and large-scale simulation or protein-folding style work leans heavily on ML infrastructure. We coordinate across these specialisms rather than treating a science hire as siloed.

Not ready to submit a brief?

Let's just talk it through. Tell us what you're building and we'll help you work out what the hire actually looks like.

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Locations

UK Office
St Albans
Eleanor Ormerod House,
Victoria St, AL1 3LT

US Office
Austin, TX
9901 Brodie Lane,
Suite 160 PMB1553

Contact Us

UK +44 2080 890 188
US +1 737 225 3237
hello@enigma-rec.ai

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