MLOps Engineer

**Senior ML Infrastructure / MLOps Engineer

Location:**
SF Bay Area (On-site)

We’re representing an ambitious AI research organization building physical autonomy systems powered by large-scale ML. You’ll own the infrastructure that makes cutting-edge model development reliable, reproducible, and scalable — from training to deployment.

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The Opportunity

Be a core part of the team responsible for the machine learning foundation of a next-generation AI platform. You will help build and maintain the systems that enable performant model training, experimentation, and production workflows at scale.

What You’ll Do

• Build and maintain scalable ML infrastructure supporting training, fine-tuning, RLHF/DPO workflows, and distributed experiments.
• Develop and manage data pipelines, dataset versioning, experiment tracking, and reproducible evaluation frameworks.
• Operate containerized training and inference environments, including CI/CD automation for models.
• Partner with researchers, engineers, and systems teams to enable rapid iteration and robust deployments.

What You Bring

• Strong experience with ML infrastructure, distributed training systems, and production-grade MLOps practices.
• Familiarity with containerization, orchestration, and reproducible ML workflows.
• Hands-on in experiment management, dataset governance, and automation tooling.
• A pragmatic mindset and ability to work across research and engineering functions.

Why This Role Excites People

• Directly shape the backbone of ML systems that support real, high-impact AI research and autonomous behavior.
• Work with a tight-knit, world-class team tackling foundational problems at the intersection of ML, systems, and autonomy.
• Competitive salary, meaningful equity, and a strong benefits package.