Senior Research Engineer

Full-time · Head Office (Farringdon, London)

About Us

Basecamp Research is building frontier AI for therapeutic design. We believe the future of medicine lies in reprogramming the body to repair itself, and we design the models and the medicines to teach it how.

Our EDEN models are trained on BaseData, the world's largest proprietary genomic dataset, with over 10B genes collected through partnerships in more than 30 countries across all seven continents. This gives our AI models access to genetic diversity that doesn't exist in public databases, and enables EDEN to design cell and gene therapies, enzymes, and peptides directly from information about a disease. Our pipeline of EDEN-designed therapeutics, beginning with in vivo cell therapy, is advancing towards clinical development.

In September 2026 we closed an oversubscribed $140M Series C led by S32, with participation from NVIDIA, Anthropic's Anthology Fund, Catalio, European Tech Collective, Firebrand River Capital, King Philanthropies, NATO Innovation Fund, NVentures (NVIDIA's venture arm), Redalpine, Rockefeller Foundation, Singular, Sovereign AI and True Ventures. We have offices and labs in London and Boston, we partner with biopharma companies and academic institutions worldwide, and our work has been recognised by Fast Company's Top 10 Most Innovative Companies in Biotech, the FT-backed Sifted AI100 list of Europe's leading AI startups as well as Barclays Eagle Labs Ones to watch, AI100.

At Basecamp Research, we pride ourselves on being a lean collaborative and global team with extremely specialist and unique skills. Our biologists, engineers, ML scientists and field explorers are united by a sense of adventure and the belief that AI, biology and data, will result in new medicines for patients who have few options today.

The Role

We are looking for a Senior Research Engineer to join our AI Research team in London. You will build the systems our research runs on and the models themselves, working as a full member of the research team rather than in support of it.

The work spans the whole path from raw biological data to a model that produces something a biologist can test. That means curating and versioning training data at petabyte scale from the world's richest metagenomic collection; running large training jobs reliably and efficiently; implementing new architectures and the ablations that tell us whether they work; and building the evaluation and inference pipelines that turn a checkpoint into designed sequences our partners can take into the lab. Evaluation in particular is an open research problem here, not a solved engineering task.

The scale is unusual and so are the problems: custom architectures on novel data modalities, training runs that push hardware limits, and a pace of experimentation that demands robust and flexible tooling.

You will sit within the AI Research team, understand the science, and make decisions that shape what research is possible and how fast ideas move from whiteboard to result. You'll report to the Head of AI Research and work closely with AI Researchers and other teams covering genomics, computational biology, and the broader platform engineering.

About You

PhD in computer science, physics, mathematics, or a related field, or equivalent depth of experience gained through building AI systems at scale.

You have built systems that made real research and taking it to production possible, in a team where engineering was treated as a first-class contribution. That might have been the data pipeline behind a large training corpus, the infrastructure for a major training run, the implementation of an architecture that ended up in a paper, or the evaluation framework a team made decisions with.

You have genuine depth in at least one of: large-scale data engineering, distributed training and accelerator performance, model implementation and experimentation, or evaluation and inference systems. You are willing to go as deep as the problem requires, including into CUDA/XLA kernels and compilers when that's where the bottleneck is.

You care about research outcomes as much as system uptime. You form opinions about what experiments to run and how to design them. You can read a paper and work out what it would take to implement it efficiently and whether it's worth doing.

You have strong software engineering practices: clean code, good testing habits, a focus on performance, and an instinct for building systems that other people can actually use. In a small team everyone depends on what you build.

You are genuinely curious about biology. The data you'll work with encodes billions of years of evolution, drawn from ecosystems most datasets never touch. You should find that interesting and not incidental.

You put the team first, have 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

Quantitative depth from a background in mathematics, physics, or a similar field. The best research engineers bring that intuition to system design decisions.

Contributions to open-source ML frameworks or research codebases, such as PyTorch, JAX, or MLX.

Experience with biological data: genomic sequences, protein structures, molecular data, or similar.

Familiarity with our broader tech stack: Kubernetes, Dagster, or similar orchestration and infrastructure tools.

Publications at major venues (e.g., NeurIPS, ICML, ICLR, or JMLR).

What we offer in return

• Impactful Mission: This is a rare chance to do work that genuinely matters. You'll join a talented, fast-moving team, access unique biological datasets at scale, and see your contributions shape real breakthroughs in AI and curative therapeutics..

• Collaborative Culture: You'll be surrounded by world-class engineers, scientists, and researchers who care deeply about their work and about each other. With offices in London and Boston, we've built a flexible, cross-functional environment where personal development and real ownership aren't just talking points.

• High Growth: We truly believe in investing our people we make coaching available to team members during steep growth journeys and we have twice yearly promotion opportunities. People who are really successful here own it and go directly to solve problems at pace and we ensure reward increases with impact.

• Comprehensive Benefits: We've built a benefits package people value. That means competitive salary, equity, and private healthcare with no medical history exclusions so strong that means most employees' family members opt into our plan over their own. We also offer Carrot Fertility with IVF stipend, salary sacrifice pension, bike to work scheme, life insurance, and more.

We are committed to equal opportunity employment regardless of ethnic or national origin, race, religion, sex, age, citizenship, sexual orientation, marital status, disability, gender identity or any other basis. If you have a disability or additional need that needs accommodating do let us know.