AI Lead – Cancer Vaccines (Clinical Stage)
Oxford (Hybrid) | up to £100k | Quick interview process | Generous pension | Visa/relocation support for strong candidates | Access to proprietary data taken from ongoing human trials
You’d be working on models that directly influence what goes into human clinical trials.
This is a small Oxford-based biotech (~20 people) using AI to design cancer vaccines aimed at preventing recurrence. They’ve recently closed a new funding round and are now entering first-in-human Phase I/II trials.
The role is hands-on. You’ll take ownership of the antigen discovery platform, applying ML to proteomic and immunologic datasets to identify targets that actually make it into patients.
What’s different here is the data. Rather than relying purely on inferred signals (like RNA), the team uses mass spectrometry to capture what is actually presented to the immune system. The models are trained much closer to biological reality, with outputs feeding into live trials and data coming back from patients.
You’ll be working closely with brilliant people, in a welcoming, highly technical environment, with full ownership of modelling direction.
What they’re looking for
Strong ML capability is the priority. This could come from deep learning, probabilistic modelling, or representation learning applied to complex datasets.
Experience with biological data (proteomics, RNA-seq, immune datasets) is helpful but not essential. The biology can be learned, the modelling needs to be strong.
Why this role is worth considering
Most AI roles in this space sit in research loops or offline evaluation. This one doesn’t.
There’s a direct line between what you build and how patients are treated, with real clinical feedback shaping the models over time.
It suits someone who wants ownership, a tight feedback loop, and work that moves beyond theory into application.
Logistics and process
Oxford-based, typically 2–3 days onsite, but open to flexibility if necessary
Relocation and visa support available
Process is 2–3 stages, discussion-led rather than heavy technical testing
1) conversation with hiring manager
2) Onsite – short presentation by candidate with QA
3) optional – conversation with the founders, more so for them to explain the strategic vision and plans for the company.
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