Machine Learning Scientist, Reinforcement Learning
About The Position
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 and have raised over $150M to date. We are seeking a motivated and creative Machine Learning (ML) Scientist to lead research into reinforcement learning for biomolecular design. This role offers the chance to work at the forefront of generative modeling research, encompassing language processing, representation learning, and protein engineering. The ideal candidate is a self-directed researcher capable of rapidly prototyping and evaluating new models and algorithms in the biomolecular domain. As an early employee, you will play a key role in shaping the direction of our machine learning initiatives and will collaborate with diverse teams of computational and experimental scientists.
Requirements
- 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
Nice To Haves
- 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
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
Benefits
- 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
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
