Senior DevOps Engineer, ML Infrastructure

Senior Dev Ops Engineer, ML Infrastructure at Serve Robotics

$/yr – $/yr (United States)

Remote available to qualified candidates in Canada: $130k - $160k CAD

At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses. The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries.

We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity.

• Deploy and maintain our ML training orchestration system that operates across multiple platforms.
• Manage cloud and on-premise environments for large-scale distributed data processing and ML training/inference systems.
• Automate deployment pipelines, monitoring, and alerting for ML and data services.
• Collaborate closely with data scientists, ML engineers, and autonomy teams to streamline experimentation and model deployment.
• Maintain and improve CI/CD systems to support rapid development and testing.
• Implement best practices for system security, reliability, and observability.
• Optimize infrastructure costs and ensure efficient resource utilization.
• Support internal developer productivity through tooling, documentation, and support.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent experience.
• 5+ years of experience as a Dev Ops, SRE, or Infrastructure Engineer, preferably supporting ML or data-intensive systems.
• Strong experience with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker).
• Proficiency in infrastructure-as-code tools such as Terraform or Helm.
• Solid understanding of CI/CD systems (Git Lab CI, Jenkins, Argo CD, etc.).
• Experience with Python and SQL.
• Experience with cloud security, IAM (Identity and Access Management), and access control.
• Experience analyzing and optimizing hardware performance.
• Experience with GPU cluster management.

• Experience managing large-scale distributed data processing systems.
• Experience analyzing and optimizing ML training workloads.
• Background in observability stacks (Prometheus, Grafana, ELK, Open Telemetry).
• Contributions to open-source Dev Ops or ML infrastructure projects.

Mid‑Senior level

Full‑time

Engineering and Information Technology

Technology, Information and Internet

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