Machine Learning Researcher

Prima Mente’s goal is to deeply understand the brain, to protect the brain from neurological disease and enhance the brain in health. We do this by generating our own data, building brain foundation models, and translating discovery to real clinical and research impact.

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.

1 month:

• 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.

3 months:

• 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).

6 months:

• 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 your work to people with very different backgrounds.

• Able to stay with hard problems for a long time, and to make progress even when the path isn’t obvious.

We don’t expect you to check every box. Strong applicants often have depth in some of these and interest in growing into others.

• Strong background in machine learning or a closely-related field (e.g. deep learning, statistics, optimisation).

  • Industry, academic, or hybrid paths are all welcome.

• Demonstrated experience training and evaluating modern ML models (e.g. transformers, diffusion, graph models, sequence models).

• Solid software skills in Python and at least one major ML framework (PyTorch, JAX, or TensorFlow).

• Experience designing and running non-trivial experiments: controlling for confounders, building robust baselines, and doing thorough error analysis.

• Ability to write clearly – whether in code comments, research docs, or papers.

• At least one of the following (more is a plus, not a requirement):

  • Experience with large-scale data (e.g. 100B+ tokens or equivalent) or distributed training.

  • Background in computational biology, genomics, epigenomics, neuroscience, or related areas.

  • Work on foundation models (language, vision, or multi-modal) and interest in applying that to biology.

  • Infra/optimisation experience (e.g. FSDP/ZeRO, quantisation, compilation, custom kernels) – especially valuable, but not mandatory.

If you’re unsure whether you “count” as an engineer or a researcher: please apply. We care about what you can do and how you think, not your current job title.

Based in San Francisco, US or London, UK. We support visa applications.

What we are doing is extremely hard. Prima Mente is for great people. We are team players who appreciate challenges, want to be hands-on, and thrive on curiosity by throwing away assumptions. We are focused on excellence at pace and huge personal growth. We are strong communicators who are highly disciplined and rigorous.

Prima Mente operates with a flat organizational structure. We gain and share knowledge by contributing to multiple opportunities. Leadership is given to those who show initiative and consistently deliver excellence.

We arrange our lives so we can work in person as much as possible.

• The solutions we build demand uncompromising quality and rigour.

• The problems we are solving are grave and present.

• We embrace curiosity and creativity.

• Every question is a path to a transformational breakthrough.

• We practice unwavering honesty and transparency in all our challenges and interactions.

• Every individual in our team is celebrated for their identity, uniqueness, and experiences.

• We are invested in each one’s bespoke personal development.

• Nurturing individuality will supercharge our collective purpose and spirit.

• We have a steadfast commitment to improve the health and well-being of patients globally.

• Every experiment run, every dataset analysed, and every innovation developed, is a step towards achieving a scalable impact.