About the role
Profluent is an AI-first protein design company. Founded in 2022, we develop deep generative models to design and validate novel, functional proteins to revolutionize biomedicine. Based in Emeryville, CA, we are backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures, and have 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 (but not required) Familiarity with foundational biology of proteins and nucleic acids Experience developing machine learning models for proteins (language models, structure prediction, design) Experience with cloud compute platforms (GCP, AWS, Azure, OCI) Previous experience in data extraction and curation from bioinformatics data sources Familiarity with wet lab experimental assays and associated limitations What We Offer High-growth opportunity with meaningful impact on the future of protein design Competitive
