Machine Learning Research Engineer

Prima Mente • San Francisco, CA

21d

About The Position

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. 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: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 developed a high-performance version of a foundation model, implemented key algorithmic optimizations that boost scalability and throughput, and published internal benchmarks demonstrating significant research impact. Why Join Us: Meaningful Impact: Contribute directly to research infrastructure that powers discoveries potentially impacting millions of lives. Innovation & Autonomy: Work at the forefront of AI and multi-omics, with the freedom to propose and implement state-of-the-art infrastructure solutions. Exceptional Team: Collaborate with talented colleagues from diverse backgrounds across ML, bioinformatics, and engineering. Growth Opportunities: Continuous learning and growth opportunities in a rapidly advancing technical field. You want to redefine what’s possible at the frontier of AI and biology.

Requirements

• 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 utilization.
• Skilled in designing and implementing scalable data pipelines capable of rapid ingestion, transformation, and processing.
• Deep expertise in modern ML frameworks and tools (e.g., PyTorch, JAX, TensorFlow), and familiarity with state-of-the-art training and inference workflows.
• Skilled in clearly articulating complex ideas, effectively communicating why particular approaches succeed or fail, and providing insightful critical analyses.
• Experience of building highly collaborative research teams.
• Track record of working on hard problems for long periods of time.
• High agency with the ability to jump on any task as needed.
• Demonstrated experience training, optimizing, and deploying large-scale models (7B+ parameters).
• Low level algorithm optimisation
• quantization (8bit or lower)
• JIT compilation
• XLA/Mosaic/Triton/CUDA
• Hardware optimisation (GPU/TPU/HPU)
• Finetuning Optimization (QLora, QDora)
• Large scale data above 2T tokens

Responsibilities

• 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:noise ratio.

What This Job Offers

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