Machine Learning Scientist

Profluent is the frontier AI lab for biology. Profluent builds powerful foundation models for all of life's molecules, unlocking solutions that transform medicine, agriculture, and beyond. Founded in 2022 and headquartered in Emeryville, CA, Profluent is backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures and has raised over $150M to date.

We're looking for a motivated and creative Machine Learning (ML) Scientist to drive research into reinforcement learning for biomolecular design. This position offers an opportunity to work at the forefront of generative modeling research across language processing, representation learning, and protein engineering. You should be a self-directed researcher who has the ability to rapidly prototype and evaluate new models and algorithms in the biomolecular domain.

As an early employee, you will proactively shape the direction of our machine learning efforts and collaborate across diverse teams of computational and experimental scientists.

Responsibilities

• Design and develop state-of-the-art online and offline reinforcement learning algorithms for protein design
• Collaborate across the machine learning and protein design teams to adapt and improve reinforcement learning techniques from other domains to protein design
• Architect, implement, and optimize core infrastructure to support the post-training of protein language models
• Curate relevant datasets and design tasks for rigorous evaluation of generative models
• Implement, analyze, and interpret multiple computational approaches and present results to colleagues in regular update meetings
• Work within a collaborative, fast-paced, interdisciplinary team across biology and machine learning to help shape the scientific and strategic vision of the company

Qualifications

• PhD (or equivalent industry experience) in Computer Science, Machine Learning, Natural Language Processing, Applied Math, Computational Biology, Statistics, or a related field
• Experience with conceiving of, implementing, and evaluating novel machine learning and reinforcement learning techniques
• Publications at major machine learning conferences (NeurIPS, ICML, ICLR) or scientific journals (Nature, Science, Nature Biotech, Nature Methods, PNAS)
• Experience with modern deep learning frameworks such as Pytorch or Jax

Preferences

• Familiarity with foundational biology of proteins and nucleic acids
• Experience developing machine learning models for proteins (language models, structure prediction, design)