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

Fulltime Machine Learning Data Science Mlops

$250k - $300k · Senior Fully remote

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AI/ML Scientist - Build the Future of Physics AI- $250,000 to $275,000 Salary You will be part researcher, part build...

Fulltime Machine Learning Ai

$250k* · Senior Fully remote

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MLOps Engineer Kubeflow GCP

Saragossa

Live

$250k - $300k · Senior Full-time Posted 7 days ago 1 min read

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

In this role, you’ll enable ML engineers and data scientists to seamlessly train, track, and deploy models by building robust, Kubernetes-based infrastructure. Responsibilities include automating training pipelines (Kubeflow), optimizing cloud infrastructure (GCP), and writing production-level code (Python, Go) with velocity. The work blends cloud-native development, distributed systems engineering, and applied AI infrastructure.

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.

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MLOps Engineer Kubeflow GCP

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