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# Senior DevOps Engineer

- **Company:** Rhoda ai
- **Location:** Mountain View, CA, United States
- **Experience:** Expert
- **Salary:** $175,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Cloud Computing, Communications Protocols, Continuous Integration, Software Design Patterns, Linux, DevOps, Domain Name System (DNS), EtherCAT, Python (Programming Language), Modbus, Networking Basics, Software Construction, Software Engineering, TCP/IP, Rust (Programming Language), Firewalls (Computer Science), Build Management, Containerization, Kubernetes, Machine Learning Operations, Grpc, Docker, Golang, Programming Languages
- **Published:** July 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=fbeebeca9bc926f0

## About the Role

* 5+ years in DevOps / 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

## Description

* 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


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