About the job
Join Prima Mente
At Prima Mente, we are pioneers in the field of biology-focused artificial intelligence. Our mission is to generate unique datasets, develop versatile biological foundation models, and translate scientific breakthroughs into real-world clinical applications. Our primary focus is on understanding the brain in-depth, safeguarding it from neurological disorders, and enhancing its function during health. Our dynamic team of AI researchers, experimentalists, clinicians, and operational experts are strategically located in London, San Francisco, and Dubai.
Your Role: Foundation Models for Biology
As a Machine Learning Engineer, you will be instrumental in the design, implementation, and scaling of foundational AI models and infrastructure for multi-omics at an unprecedented scale. Your contributions will facilitate significant advancements in scientific comprehension and lead to groundbreaking applications in the medical and biological fields.
Key Responsibilities:
- Develop high-performance machine learning algorithms optimized for large-scale applications, ensuring utmost reliability and efficiency.
- Design, implement, and maintain comprehensive experimentation pipelines that allow for rapid iterations, precise assessments, and reproducible research results.
- Refactor and enhance prototype research code into clean, maintainable, and efficient repositories prepared for production-level deployments.
- Create fast data processing workflows that can effectively manage extensive datasets to expedite research and model development.
- Engage in experimental design, with a focus on high-impact experiments that yield the greatest signal-to-noise ratio.
Growth Expectations
In 1 month, you will initiate initial experiments utilizing state-of-the-art machine learning models, review and apply advanced research papers, and enhance existing code for improved efficiency and precision.
By 3 months, you will take ownership of a prototype model architecture, showcasing notable algorithmic enhancements, and contribute to methods for large-scale data ingestion and training.
Within 6 months, you will have significantly impacted the implementation of a high-performance foundation model, incorporating key algorithmic optimizations that improve scalability and throughput, along with publishing internal benchmarks that demonstrate substantial effects.