Machine Learning Scientist Reinforcement Learning
AI-first protein-design company using generative foundation models and wet-lab validation to create functional proteins and genome-editing systems.
Funding history
About Profluent
Profluent develops AI models for de novo protein design, with applications in biomedicine, agriculture, and industrial biology. Its platform includes protein-generation models such as ProGen3 and the AI-designed OpenCRISPR-1 gene editor.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will research, prototype, evaluate, and improve reinforcement learning methods for biomolecular and protein design. You will build post-training infrastructure, curate evaluation datasets, analyze computational approaches, present findings, and collaborate across machine learning and protein design disciplines.
Requirements
- PhD or equivalent industry experience in a relevant technical or scientific field
- Experience conceiving, implementing, and evaluating novel machine learning and reinforcement learning techniques
- Publications at major machine learning conferences or scientific journals
- Experience with PyTorch or JAX
- Legal authorization to work in the United States
Responsibilities
- Design and develop online and offline reinforcement learning algorithms for protein design
- Adapt and improve reinforcement learning techniques for protein design
- Architect, implement, and optimize infrastructure for post-training protein language models
- Curate datasets and design evaluation tasks for generative models
- Implement, analyze, interpret, and present computational approaches
Benefits
- Equity participation
- 401(k) with employer match
- Health, dental, and vision insurance
- Generous PTO policy