Senior DevOps Engineer

10Beauty is a robotics startup based in Burlington, MA, focused on building the world’s first fully autonomous manicure machine. They are seeking a Senior DevOps Engineer to build, automate, and maintain the infrastructure for their robotic machines, while collaborating with engineering teams to define their DevOps and MLOps strategy.

Responsibilities:

• Build & Maintain CI/CD Pipelines: Architect and manage robust CI/CD pipelines for both our embedded software and our machine learning models, ensuring seamless integration, testing, and deployment
• Fleet Management & OTA Updates: Develop and maintain the infrastructure for secure and scalable Over-the-Air (OTA) firmware updates to our fleet of robotic machines deployed in the field
• Infrastructure as Code (IaC): Manage and provision our cloud infrastructure (AWS), ensuring it is repeatable, scalable, and secure
• Containerization & Orchestration: Deploy and manage containerized applications using Docker and Kubernetes
• Monitoring & Alerting: Implement and maintain comprehensive monitoring, logging, and alerting systems to ensure the health, performance, and reliability of our entire stack, from the cloud to the robotic machines themselves
• Collaboration & Planning: Work closely with our ML, embedded, and software engineering teams helping to define our long-term DevOps and MLOps strategy

Requirements:

• 5-8 years of professional experience in a DevOps, SRE, or similar role
• Hardware/robotics experience: You have experience with the unique challenges of embedded systems, managing OTA updates, and deploying software to physical devices in the field
• Strong proficiency with cloud platforms: AWS, GCP, or Azure (AWS preferred)
• Expertise with CI/CD tools: Experience with Jenkins, GitLab CI, GitHub Actions, or similar platforms
• Proficiency in containerization: Strong skills with Docker and Kubernetes
• Hands-on experience with IaC: Terraform is a plus
• Excellent scripting skills: Python or Bash
• Experience with monitoring tools: Prometheus, Grafana, Datadog, or similar
• A proactive, problem-solving mindset and a passion for building scalable, reliable systems
• MLOps: Automate the end-to-end ML lifecycle, including model versioning, training pipeline automation, model serving, and continuous monitoring of model performance in production
• MLOps expertise: A proven track record of building and managing pipelines for the deployment and monitoring of machine learning models in a production environment