Senior Research Scientist AI Research

1000scholars; Greater London; On-site; GBP 110,000 - 170,000; Full time

Job summary

Basecamp Research in London seeks a Senior Research Scientist to join its AI Research team, combining mathematics and machine learning to push biological AI forward. You will own research directions, publish in top venues, and translate findings into production-ready models, working with genomics, bioinformatics, and engineering teams.

The ideal candidate holds a PhD in a quantitative field, has a strong publication record, and thrives in frontier research with cross-disciplinary collaboration.

Qualifications

• PhD in mathematics, physics, or related quantitative field.
• Strong publication record in top ML venues (e.g., NeurIPS, ICML, ICLR).
• Experience with frontier research and large-scale experimentation.

Responsibilities

• Shape a research direction within the AI Research team.
• Publish results at top venues and translate findings into models.
• Collaborate with genomics, sequence modeling, and engineering teams.

Skills

Mathematics

Machine Learning

Research Leadership

Publication record

Education

PhD in quantitative field

Job description

Senior Research Scientist AI Research

Basecamp Research

London, United Kingdom

The Role

We are looking for a Senior Research Scientist to join our AI Research team in London. You will work at the intersection of mathematics and machine learning, bringing rigorous quantitative thinking to some of the most challenging problems in biological AI.Our research group is small, collaborative, and operates more like a frontier research lab than a typical biotech team. We train large-scale foundation models on the world's largest and richest metagenomic datasets, and we look for people who can push the mathematical boundaries of what these models can learn and represent. The work requires developing new methods and theories from scratch.This is a role with real ownership. You will shape a research direction within the team, publish at top venues, and see your work translated into models that generate genuinely novel biology. The problems are hard, the data is unique, and the impact is tangible.You'll be reporting to the Head of AI Research and working alongside researchers covering genomics, sequence modeling, and large-scale deep learning, as well as biologists, bioinformaticians, and engineers across the wider company.

About You

PhD in mathematics, physics, or a closely related quantitative field. Your work in AI and machine learning is driven by curiosity, and you've brought a depth of mathematical thinking that clearly distinguishes your work.

You have spent meaningful time on frontier research and you already have an understanding of how top research teams operate, from problem selection to large-scale experimentation to shipping results that matter.

Your research interests might span generative models, geometric deep learning, probabilistic modeling, dynamical systems, information geometry, optimization theory, or related areas. What matters most is that you think from first principles and bring mathematical structure to hard problems.

You have a strong publication record at top ML venues (NeurIPS, ICML, ICLR, or equivalent) that demonstrates independent thinking and a coherent research arc.

You are genuinely curious about biology. Domain expertise is welcome, and what we really care about is that you find it exciting to work with data from unexplored ecosystems encoding billions of years of evolutionary optimization. The best candidates will see biological data as a source of fundamentally new scientific questions.

You have low ego, strong collaborative instincts, and a startup mentality. You are comfortable with ambiguity, willing to iterate fast, and happy to work in a small team where everyone contributes across boundaries.

Nice to Have

• Experience training models at large scale (billions of parameters, extreme long context, distributed training, custom architectures).
• Domain knowledge in life sciences, genomics, or computational biology, e.g., familiarity with protein structure, molecular dynamics, or related biophysical methods.