Member of Scientific Staff

Applied Geometry; Greater London; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Competitive pay

Equity package

Private medical and dental cover

Budget for tooling and infrastructure

Conference funding

Team meals

Flexible time off

Job summary

Applied Geometry is an AI research lab building biological superintelligence. We train agents to reason from incomplete evidence, and discover biology and therapies. This hands‑on scientific role involves working with founders and engineers on challenging, high‑impact problems.

As an early team member you’ll define rigorous tasks, curate data, and implement reproducible analyses in Python or R while helping shape our scientific standards and culture.

Qualifications

• PhD in biology or closely related field with deep domain knowledge.
• Strong judgement and ability to evaluate evidence.
• Hands-on experience turning raw data into reproducible conclusions.
• Experience with Python or R for data analysis.

Responsibilities

• Design scientific tasks grounded in real research questions.
• Curate experimental datasets and identify uncertainties.
• Build reproducible analysis pipelines in Python or R.
• Develop defensible reference solutions and evaluation criteria.
• Stress-test agent reasoning and catch non-robust conclusions.
• Collaborate with research engineers to translate tasks into RL environments.
• Help define scientific standards and priorities.

Skills

Deep biology

Scientific judgement

Hands-on research

Quantitative skills

AI-agent fluency

High agency

Intellectual honesty

Education

PhD in biology

Computational biology

Bioinformatics

Tools

Python

R

Reinforcement Learning

Job description

Applied Geometry

Applied Geometry is an AI research lab building biological superintelligence: AI that can make discoveries in biology and health beyond the reach of the best human research teams. Our ambition is to compress years of scientific discovery into days and transform our ability to understand and cure disease.

We're training frontier models to do science across biology, drug discovery and clinical medicine. We believe the next frontier of AI requires systems that can reason from incomplete evidence, generate and test hypotheses, and pursue complex research programmes.

Our Approach

We're building on the methods that have driven extraordinary advances in coding and mathematics, extending them to the harder problems of biology and human health. We develop reinforcement learning environments grounded in real scientific data and expert knowledge, giving AI agents the tools to tackle consequential biomedical problems and using their performance to improve the underlying models.

As these systems become more capable, we're training them to sustain coherent research over longer periods: uncovering disease mechanisms, designing experiments and developing new therapies. Our goal is to build AI researchers whose discoveries exceed what their creators could have anticipated.

We've raised a large funding round from leading Silicon Valley investors to achieve this goal.

The Role

As a Member of Scientific Staff, you'll define what rigorous scientific reasoning and meaningful progress look like for AI agents working in your domain. Your scientific judgement will directly shape the tasks they learn from, the conclusions they're rewarded for, and the capabilities we build.

We're hiring a scientist with a PhD in biology, computational biology, bioinformatics, or a related field, with real depth of biological understanding and with the curiosity to work across disciplines.

You'll work directly with the founders and research engineers in a small team, with substantial autonomy and responsibility from day one. This is a hands‑on scientific role: you'll analyse real data, build defensible reference solutions, and interrogate agent reasoning with the scrutiny you'd bring to a consequential research decision.

What you'll do

• Design challenging scientific tasks grounded in real research questions, incomplete evidence, and the decisions scientists face in practice.

• Curate experimental datasets and supporting evidence, identifying limitations, confounders, and sources of uncertainty that affect their interpretation.

• Build reproducible analysis pipelines in Python or R to establish reference results and test whether conclusions hold across replicates, methods, and analytical choices.

• Develop scientifically defensible reference solutions and evaluation criteria that recognise valid alternative approaches and distinguish plausible answers from conclusions the evidence actually supports.

• Stress‑test agent reasoning step by step, catching errors that sound textbook‑correct but would not survive a lab meeting or project review.

• Evaluate whether agents know when to draw a conclusion, when to express uncertainty, and which experiment or analysis would resolve it.

• Work closely with research engineers to turn scientific tasks into RL environments and translate agent failures into improvements in training and evaluation.

• Help define Applied Geometry's scientific standards, research priorities, and culture as an early member of the team.

What we're looking for

• Deep biological expertise — You hold a PhD in biology, computational biology, bioinformatics, pharmacology, immunology, oncology, or a closely related field. You have substantial depth in your area and understand where established knowledge ends and unresolved questions begin.

• Sharp scientific judgement — You can spot artefacts, confounders, inappropriate controls, and claims the evidence doesn't support. You distinguish statistical significance from biological meaning and know what would make a conclusion defensible.

• Hands‑on research ability — You've personally analysed and interpreted experimental data. You can take a question from raw evidence to a reproducible, scientifically justified conclusion.

• Quantitative confidence — You can build and critically assess analysis workflows in Python or R, choose appropriate statistical methods, and explain how analytical choices affect the result. The depth of programming required will reflect your scientific specialism.

• AI‑agent fluency — Candidates who already use AI agents daily in their research are strongly preferred. You experiment with their capabilities, verify their outputs, and are confident challenging them when the evidence demands it. Prior RL or agent infrastructure experience is not required; we'll train you on our methods and systems.

• High agency — You thrive in ambiguity, take ownership of difficult problems, and turn ideas into useful work. You're comfortable working directly with founders, setting priorities, and being judged by the quality of your contribution.

• Intellectual honesty and ambition — You hold a high bar for evidence, update your views when you're wrong, and want to build AI capable of consequential scientific discovery.

We welcome candidates from academia and industry. Experience advancing drug discovery, preclinical or clinical programmes is valuable, as is deep academic expertise combined with strong quantitative skills and AI fluency.

What's in it for you

We want Applied Geometry to be the best place in the world to do ambitious AI biology work. Early hires will receive exceptional support and significant ownership.

Benefits include:

• Competitive, top‑of‑market compensation.

• A significant equity package to share in the upside of building a category‑defining company.

• Comprehensive private medical and dental coverage.

• A significant budget for best‑in‑class equipment, research tooling, software, and infrastructure.

• Access to the compute and petabytes of proprietary biomedical data required to train ambitious models.

• Fully funded opportunities to attend leading AI, biology, and scientific conferences.

• Regular team meals, a stocked office, and a working environment designed for exceptional work.

• Flexible time off with no strict limits.