MLOps Engineer

About

Interested in building the foundational machine learning infrastructure for next-generation Physics AI software?

The environment is deeply technical, blending computational physics, high-performance computing, and cloud-native software development.

Responsibilities

• Enable ML engineers and data scientists to seamlessly train, track, and deploy models by building robust, Kubernetes-based infrastructure.
• Automate training pipelines.
• Optimize GCP infrastructure.
• Write production-level code (Python, Go) with velocity.
• Blend cloud-native development, distributed systems engineering, and applied AI infrastructure.

Requirements

• Hands‑on experience building on Kubernetes.
• Experience deploying ML models.
• Experience working with cloud infrastructure tools like Terraform and Docker.
• Familiarity with GCP is a plus.
• Genuine interest in Physics.
• Ability to operate in a startup environment.

Compensation

• Flexible compensation depending on experience and expectations.
• Typically ranging from $250k–$300k base plus equity with significant upside.

Location

• Full‑time position based in the San Francisco Bay Area.

Application Process

• No resume required.
• No C2C.

If you’re excited about building large‑scale ML infrastructure and enabling the next generation of physics‑based models, we’d love to connect.