Machine Learning Researcher

About Prima Mente

Prima Mente is a frontier biology AI lab. We generate our own data, build general purpose biological foundation models, and translate discoveries into research and clinical outcomes. Our first goal is to tackle the brain: to deeply understand it, protect it from neurological disease, and enhance it in health. Our team of AI researchers, experimentalists, clinicians, and operators is based in London, San Francisco and Dubai.

As a Machine Learning Researcher, you will help design, train, and evaluate foundation models that learn from large-scale biological data (genomics, epigenomics, single-cell, proteomics, clinical signals).

Depending on your strengths, you might skew more towards:

• Modelling & algorithms – new architectures, training objectives, scaling strategies, multi-task / multi-modal learning.
• Applied research – framing high-impact questions with clinicians and biologists, building end-to-end disease models, and stress-testing them on real data.
• Analysis & insight – probing model internals, interpretability, mechanistic understanding, biomarker discovery.
• Systems & efficiency – if you enjoy it, helping push training, data, and inference infrastructure to the next scale.

The role is deliberately broad: we’re looking for exceptional ML talent with strong research instincts, not a single CV template.

You won’t do all of these on day one; think of this as the space of things you may own.

• Design and implement ML models for large-scale biological data, from pre-training to task-specific fine tuning.
• Partner with biologists, clinicians, and data scientists to translate biological and clinical questions into tractable ML problems.
• Run end-to-end experiments: dataset curation, training, evaluation, error analysis, and iteration.
• Develop and refine evaluation suites for robustness, generalisation, and clinical relevance (e.g. across cohorts, sites, populations).
• Explore multi-modal and multi-task training across genomic, epigenomic, transcriptomic, proteomic and clinical signals.
• Perform in-depth model analysis to extract mechanistic or biomarker-level insights, not just metrics.
• Collaborate on papers, internal memos, and external communication of key research results.
• (Optional / plus) Contribute to scaling and optimisation of training and data pipelines, in close collaboration with research engineers.

This is illustrative; we know great people ramp differently.

• You’ve reproduced key baselines, run initial experiments on our internal datasets, and are comfortable with our training stack.
• You’ve shipped your first improvements (e.g. better objective, data pre-processing, or evaluation variant) and presented results to the team.

• You own a research thread: a model family, disease application, or methodological idea.
• You’re independently designing experiments, refining hypotheses, and coordinating with relevant partners (ML, wet lab, clinical).

• You’ve delivered meaningful research impact: a stronger model, a new capability, a better biomarker, or evidence that changes our direction.
• You are a go-to person for your area, helping others design experiments, debug models, and evaluate results.

• Direct patient impact:
  Your work sits on the critical path to earlier detection and better treatment of devastating brain diseases.
• End-to-end environment:
  We run the full stack from data generation to models to clinical studies, giving you an unusually tight feedback loop.
• Exceptional peers:
  You’ll work with a small, high-calibre team across ML, biology, and clinical medicine.
• High autonomy, high bar:
  You’ll have genuine ownership over problems that matter, with the expectation of operating at a very high standard.

You likely recognise yourself in several of these:

• Motivated by advancing human health through AI, especially in neuroscience and complex disease.
• Deeply curious, with a habit of reading papers, prototyping ideas, and stress-testing your own assumptions.
• Comfortable doing real engineering work in service of research – but see yourself first and foremost as a researcher.
• Enjoy collaborating across disciplines and explaining…