Machine Learning Researcher

Prima Mente • San Francisco, CA

2d

About The Position

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. Role focus 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.

Requirements

• 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.
• 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.

Responsibilities

• 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.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed