Team Lead AI/ML Scientist

Discover International • United State
Relocation
Apply
AI Summary

Lead ML engineers and scientists in AI for materials composition, structure, and performance. Oversee development of deep learning tools for discovery, synthesis, and performance prediction. Collaborate with experimental teams to design AI-powered lab orchestration and synthesis optimization.

Key Highlights
Lead ML engineers and scientists
Develop deep learning tools
Collaborate with experimental teams
Key Responsibilities
Lead ML engineers and scientists
Develop deep learning tools
Collaborate with experimental teams
Technical Skills Required
Python PyTorch Physics-informed modeling Cloud computing (AWS) High-throughput experimental workflows
Benefits & Perks
Relocation package
Hybrid work arrangement

Job Description


We're looking for an experience Team Lead AI/ML Scientist ideally based in Boston! (Relocation Package also offered) as this role is Hybrid (2 days on-site in Cambridge, Mass)


Required Skills:

  • Proven leadership in AI/ML for materials science, chemistry, or physics.
  • Expertise in deep learning, PyTorch, and physics-informed modeling (e.g., PINNs, neural ODEs).
  • Strong Python skills and a track record in scientific publishing or open-source AI tools.
  • PhD in Computer Science, Applied Mathematics, Materials Science, or similar.
  • Experience with cloud computing (AWS) and high-throughput experimental workflows.
  • Knowledge of integrating ML into lab operations and accelerated discovery platforms.
  • Join us and redefine what's possible at the intersection of AI and materials science!


What You’ll Do

  • Lead ML engineers and scientists pushing boundaries in AI for materials composition, structure, and performance.
  • Oversee development of deep learning tools for discovery, synthesis, and performance prediction.
  • Build innovative, physics-informed architectures embedding conservation laws and scientific principles.
  • Collaborate with experimental teams to design AI-powered lab orchestration and synthesis optimization.
  • Define strategies for dynamic platforms that refine models as data evolves.
  • Share groundbreaking findings with stakeholders and the global research community.


To apply - please contact Sam Shinner at Discover International


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