Profluent
Biomedicine
MachineLearningScientist,ReinforcementLearning
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Machine Learning Scientist, Reinforcement Learning at Profluent. Skills: Reinforcement learning, Machine learning, Protein design. Design reinforcement learning algorithms. Develop reinforcement learning algorithms”
Industry & Context.
Evaluate new models; Evaluate new algorithms
What They're Looking For.
Must Have
PhD or equivalent industry experience, Experience with machine learning techniques, Experience with reinforcement learning techniques, Publications at major conferences
Nice to Have
Familiarity with foundational biology, Experience developing ML models for proteins, Experience with cloud compute platforms, Experience in data extraction, Experience in data curation, Familiarity with wet lab assays
What You'll Do.
Design reinforcement learning algorithms
Develop reinforcement learning algorithms
Adapt reinforcement learning techniques
Improve reinforcement learning techniques
Architect core infrastructure
Implement core infrastructure
Optimize core infrastructure
Curate relevant datasets
Design tasks for evaluation
Implement computational approaches
Analyze computational approaches
Interpret computational approaches
Present results to colleagues
How You'll Work.
Team & Collaboration
Machine learning teams; Protein design teams; Interdisciplinary team
Communication Scope
Present results
Full Job Description
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, Statis
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