Machine Learning Engineer - Foundation Models for Biology

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 - Foundation Models for Biology

You will play a pivotal role in designing, implementing, and scaling foundational AI models and infrastructure for multi‑omics at massive scale. Your work will directly drive breakthroughs in scientific understanding and contribute to transformative applications in medicine and biology.

Key Tasks

• Implement high‑performance ML algorithms optimised for massive‑scale, ensuring reliability, efficiency, and scalability.
• Design, develop, and maintain robust experimentation pipelines enabling rapid iteration, precise evaluations, and reproducible research outcomes.
• Refactor and scale prototype research code into clean, maintainable, and performant repositories suitable for production‑grade deployments.
• Create high‑speed data processing workflows capable of efficiently handling large‑scale datasets to accelerate experimentation and model development.
• Experimental design, prioritising high impact experiments with the highest signal‑to‑noise ratio.

Expected Growth

In 1 month you will be responsible for running initial experiments with state‑of‑the‑art machine learning models, reviewing and implementing cutting‑edge research papers, and optimizing existing code for efficiency and accuracy. In 3 months you will directly own and have created a prototype model architecture, demonstrated significant algorithmic improvements, and contributed to scaling methods for large‑scale data ingestion and training. In 6 months you’ll have heavily contributed to the implementation of a high‑performance version of a foundation model with key algorithmic optimisations that boost scalability and throughput, and published internal benchmarks demonstrating significant impact.

Who You Are

You want to redefine what’s possible at the frontier of AI and biology. We don’t expect you to check every box. Strong applicants often have depth in some of these and interest in growing others.

Ideal Experience

• Deep understanding of state‑of‑the‑art machine learning methodologies and proven experience in translating them into practical solutions.
• Solid foundation in distributed computing principles, parallel processing, and algorithmic efficiency.
• Experience optimizing ML algorithms for performance, memory efficiency, and compute resource utilisation.
• Deep expertise in modern ML frameworks and tools (e.g., PyTorch, JAX, TensorFlow).
• Familiarity with state‑of‑the‑art training, optimisation, and deploying large‑scale models (7B+ parameters) and inference workflows.
• Skilled in designing and implementing scalable data pipelines capable of rapid ingestion, transformation, and processing.
• Skilled in clearly articulating complex ideas, effectively communicating why particular approaches succeed or fail, and providing insightful critical analyses.
• Low‑level algorithm optimisation quantisation (8bit or lower) JIT compilation XLA/Mosaic/Triton/CUDA
• Hardware optimisation (GPU/TPU/HPU)
• Finetuning optimisation (QLora, QDora)
• Large‑scale data above 2T tokens

Location

Based onsite in San Francisco, US or London, UK. We support O1 (US) and GTV (UK) visa applications.

Culture Insight

Prima Mente is a place where great people get to tackle great challenges together. We’re innately curious, fundamentally hands‑on and passionate about excellence. Together we build an environment where outstanding communication, scientific rigour and creative innovation come together to effect impact at pace. Come build the world’s frontier AI biology lab with us. We arrange our lives so we can work in person as much as possible.