Generative
Senior Member of Technical Staff - AI for Science

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Generative is thrilled to be partnering with an ambitious stealth AI biology company on a retained basis as they build out their founding technical team in London.
The company is building towards what they describe as biological superintelligence — developing AI systems that can learn how human biology works, reason over complex biological data and accelerate scientific discovery.
The team is developing biological foundation models trained on large-scale, proprietary multimodal human datasets, alongside AI agents and reinforcement learning systems capable of generating hypotheses, designing experiments, using scientific tools and improving through feedback.
They have raised significant backing from leading Silicon Valley investors and are now scaling an exceptionally high-calibre technical team.
🧬 The Role
As a Senior Member of Technical Staff, you'll be one of the key technical contributors shaping the company's research direction and technical foundations.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Depending on your background, you could work across:
- Biological foundation models
- Deep learning & generative modelling
- Reinforcement learning
- Agentic AI
- Multimodal ML
- Large-scale model training
- ML research & systems
This is a highly hands-on role working closely with the founding team. You'll tackle difficult problems from first principles, design and run research programmes, and help establish the technical standards of an ambitious AI research organisation from the ground up.
🔬 What We're Looking For
We're interested in exceptional ML researchers and research engineers with genuine depth in areas such as:
- Deep learning & foundation models
- Generative modelling
- Reinforcement learning
- Multimodal AI
- Large-scale ML systems
- Agentic AI
- Scientific / biological ML
Experience working with biological data is highly valuable — including single-cell, transcriptomics, proteomics, imaging, EHR, clinical trials or molecular data.
However, exceptional researchers from adjacent frontier AI domains are equally relevant. We're particularly interested in people who combine research judgement, engineering depth and high agency — individuals who enjoy working on open-ended problems without a predefined roadmap.


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⚡ What You Can Expect
- £150,000–£250,000 base salary + significant equity
- London-based, 5 days per week in the office
- Significant compute and proprietary biomedical datasets
- Best-in-class research tooling and infrastructure
- Substantial equipment and conference budget
- Private medical & dental
- Flexible time off
- Regular team meals and a highly collaborative environment
- The opportunity to help shape an early-stage technical organisation from the ground up
This is an opportunity to work at the intersection of frontier AI and biology, alongside a small, highly technical team tackling some of the fundamental challenges involved in computationally modelling human biology.
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