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Machine Learning Scientist, Reinforcement Learning

Profluent
Location
Emeryville, California, United States; Hybrid (2-3 days on-site)
Posted
2 months ago
Department
Machine Learning
What they actually want (must-haves)
  • 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 have
  • 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 the job really is

As a Machine Learning Scientist focused on reinforcement learning at Profluent, you will conduct research to develop and optimize algorithms for protein design. Your role involves collaborating with interdisciplinary teams, prototyping new models, and evaluating generative models in the biomolecular domain. You will also curate datasets and present your findings to colleagues, shaping the scientific direction 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
Things to weigh
  • No specific mention of remote work flexibility beyond hybrid model (2-3 days on-site)
  • The role is positioned at an early-stage company, which may involve uncertainty and rapid changes
  • Salary range is provided but could vary based on experience and negotiation
Job score2.8/5
Benefits3/5
Freshness1/5
Career value5/5
Role clarity5/5
Pay transparency0/5

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