MLOps Engineer

Interested in building the foundational machine learning infrastructure for next-generation Physics AI software? Interested in learning more about this job Scroll down and find out what skills, experience and educational qualifications are needed. 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, optimizing GCP infrastructure, 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 ML models, 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 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 with significant upside. If you’re excited about building large-scale ML infrastructure and enabling the next generation of physics-based models, we’d love to connect. xywuqvp No resume required. No C2C.