ML Research Engineer

Diffuse Bio
Menlo Park
Workplace: HybridFull timeUSD 90,000 - 130,000 annuallyFunction: Research & Scientific (R&D)Experience: 3+ yearsSkills: ["Problem-solving","Communication","Teamwork","Self-starter","Results-driven"]

Join a founding engineering team to extend and scale a deep generative modeling toolkit for molecular design, conducting deep learning experiments, and collaborating with software engineers to enable efficient training and deployment of models.

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Diffuse Bio
Diffuse Bio
3 years ago

ML Research Engineer

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Last checked: 16 hours agoStatus: Live

Job Summary

Join a founding engineering team to extend and scale a deep generative modeling toolkit for molecular design, conducting deep learning experiments, and collaborating with software engineers to enable efficient training and deployment of models.
Location: Menlo Park
Workplace: Hybrid
Employment Type: Full time
Job Function: Research & Scientific (R&D)

Key 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.

Pay and Benefits

Salary: USD 90,000 - 130,000 annually

Key Requirements

  • •3+ years of industry experience in a data science or engineering position.
  • •Proficient in Python with experience using deep learning frameworks (e.g., PyTorch).
  • •Track record of impressive work in ML/deep learning in industry or academia.
Experience:3+ yearsMLDeep learning
Skills:Problem-solvingCommunicationTeamworkSelf-starterResults-driven
Tech Stack:PythonPyTorchDeep Learning

Eligibility

Visa:Will sponsor
Work Authorization:Sponsorship available.

Company Brief

Diffuse Bio
Develops computational protein design platforms using diffusion and generative models to create novel proteins and biologics, aiming to accelerate therapeutic discovery and engineering by combining machine learning with biophysical design principles.
Industry: Biotech
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