Senior DevOps Engineer

At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design.

We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.

• Maintain and improve our CI/CD pipeline: instrument and monitor our CI jobs, and iteratively improve on it.
• Manage and maintain infrastructure around build artifacts and dependencies storage.
• Build and improve on our cloud and GPU cluster, and create essential devops infrastructure to support our daily research and software development effort.
• Improve our MLOps training and deployment path: checkpoint registry/DB, model artifact promotion, and rollout to inference/robot endpoints.
• Improve our infrastructure observability.
• Harden security & multi-tenancy

• 5+ years in Dev Ops / SRE / platform / infra, with ownership of production systems.
• Proficient in a programming language such as Python, C++, Rust, Go
• Strong knowledge of software engineering best practices and design patterns
• Experience with docker and containerized environments
• Experience with software build systems for cloud infrastructure and embedded systems.
• Experience with procedural CI and CD, and build artifact delivery (OTA update) system.
• Comfortable with operating in Linux environment
• Self-starter mentality — comfortable with ambiguity, able to prioritize independently, and willing to jump in wherever needed
• Effective communication skills; able to work cross-functionally in a fast-moving team

• Knowledge of industrial communication protocols (EtherCAT, Modbus, gRPC, etc.)
• Experience with ML Ops
• Understanding of networking fundamentals (TCP/IP, DNS, firewalls) and security best practices for embedded IoT devices
• Knowledge with kubernetes and cloud system orchestration