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ML Research Engineer

Diffuse Bio· Engineering
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Hybrid (Menlo Park, CA)$90k - $130k + equityFull-timePosted 11mo ago

About the role

Generative AI for protein design. Role focuses on extending and scaling in-house deep generative modeling toolkit for downstream molecular design applications, conducting deep learning experiments, and collaborating with software engineers for training and deployment.

Responsibilities

  • Extend and scale Diffuse's in-house deep generative modeling toolkit for downstream applications in molecular design.
  • Thoughtfully execute deep learning experiments to improve performance of models or develop new functionality (e.g. loop engineering, structure prediction of protein-protein complexes).
  • Work closely with software engineers to build systems for efficient training and deployment of deep learning models.

Qualifications

  • 3+ years of industry experience in a data science or engineering position.
  • Proficient in Python.
  • Experience with deep learning frameworks (e.g., PyTorch).
  • Graduate degree in math, CS, stats, bioengineering, comp bio, or related field (not required for exceptional candidates).
  • Self-starter who enjoys tough scientific problems, results-driven, capable of critical, methodical, and creative thinking about experiments.
  • Located in the Bay Area (remote work is an option for exceptional candidates).

Skills

Required:PythonDeep LearningMachine Learning

Benefits

  • The opportunity to join the founding team and play a critical and expanding role in shaping the company.
  • The opportunity to work on cutting-edge AI with leading researchers from top institutions.
Last updated Jul 8, 2026. Interested? Apply on the company site.
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About Diffuse Bio

Generative AI for protein design

Diffuse is building generative AI for protein design. Our mission is to build AI systems that engineer new and useful proteins with unprecedented control and accuracy. Our team has been behind breakthroughs in AI protein design for the past 7 years, including the first experimental validation of AI-generated proteins and diffusion models for protein structure and sequence.

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