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 compensation package with equity participation
401(k) with a strong employer match
Comprehensive benefits including health/dental/vision insurance
Generous PTO policy and commitment to work-life balance
Professional development opportunities in a cutting-edge field at the intersection of AI and biology

Profluent Bio, Inc is an equal opportunity employer promoting diversity and inclusion in the workspace. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical conditions, veteran status, sexual orientation, gender (including gender identity and gender expression), sex (which includes pregnancy, childbirth, and breastfeeding), genetic information, taking or requesting statutorily protected leave, or any other basis protected by law.

Employment Eligibility Verification

Legal authorization to work in the United States is required. In compliance with federal law, all persons hired must verify their identity and work eligibility and complete the required employment verification form upon hire.

Hiring Salary Range

$200,000—$300,000 USD

Machine Learning Scientist, Reinforcement Learning