← Serch more jobs

ML Research Scientist, Foundation Models (Senior / Staff / Principal)

LinkedIn Genesis Molecular AI New York, NY
Not Applicable Posted March 30, 2026 2 variants Job link
Thinking about this job
Not Met Priorities
What still needs stronger evidence
Requirements
  • A deep learning expert with a portfolio of novel research in one or more cutting-edge domains of generative or predictive modeling (e.g., diffusion, flow matching, RL, foundation model architectures and training methods).
  • An independent, first-principles thinker with a strong sense of ownership over your research projects and a drive to see them through to completion.
  • A strong coder, comfortable with going deep into the engineering stack to build, debug, and ship your own models.
  • A curious, problem-oriented mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries.
  • No prior experience in biology or chemistry is necessary – only willingness to learn.
  • A true team player with strong communication skills who thrives in highly collaborative, mission-driven environments where science and engineering are deeply intertwined.
  • Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.
  • Experience in distributed training and inference of large models on GPU clusters.
  • Familiarity with molecular data, (proteins, small molecules), physics-informed ML, or 3D point cloud data.
Preferred Skills
  • PhD in machine learning, computer science, other computational sciences or equivalent research experience, demonstrated by a strong publication record.
  • Hands-on experience with Pytorch, Pytorch Lightning, Ray Distributed Training, Pytorch Geometric, etc.
  • Experience in distributed training and inference of large models on GPU clusters.
  • Familiarity with molecular data, (proteins, small molecules), physics-informed ML, or 3D point cloud data.
Education
  • (Not required) – A deep learning expert with a portfolio of novel research in one or more cutting-edge domains of generative or predictive modeling (e.g., diffusion, flow matching, RL, foundation model architectures and training methods).
  • (Not required) – PhD in machine learning, computer science, other computational sciences or equivalent research experience, demonstrated by a strong publication record.