Role overview
Interested in building the foundational machine learning infrastructure for next-generation Physics AI software?
What you'll work on
The environment is deeply technical, blending computational physics, high-performance computing, and cloud-native software development.
If you have hands-on experience building on Kubernetes, deploying open-source MLOps frameworks such as Kubeflow or Argo, and working with cloud infrastructure tools like Terraform and Docker, this could be a strong fit. Familiarity with GCP is a plus, as is a genuine interest in Physics and experience operating in a startup environment.
This is a full-time position based in the San Francisco Bay Area. Compensation is flexible depending on experience and expectations, typically ranging from $250k–$300k base plus equity.
If you’re excited about building large-scale ML infrastructure and enabling the next generation of physics-based models, we’d love to connect.
No resume required.