Senior Robot Infrastructure Engineer
San Francisco, United States · Hybrid · Full-time
- Posted 1mo ago
- From Skild AI’s careers page
- Location
- San Francisco, United States
- Work mode
- Hybrid
- Type
- Full-time
- Level
- Senior
- Experience
- 3+ years
- Department
- Engineering
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About the role
Roles and Responsibilities:
- Design, implement, and maintain production-grade C++ and Go running directly on robotic platforms.
- Build on-device systems for interfacing with core robotics software, managing local data buffering, and handling edge-side communication logic.
- Architect and maintain secure, low-latency edge-to-cloud data pipelines connecting deployed robot fleets to cloud infrastructure and web platforms.
- Design, build, and operate OTA update mechanisms for safely and reliably deploying software updates to robots in the field.
- Build telemetry, alerting, logging, and monitoring tools across the robot operating system, cloud infrastructure, and fleet dashboards.
- Optimize edge software for reliability, performance, resource usage, and resilience under real-world deployment constraints.
- Collaborate with autonomy, hardware, deployment, and web product teams to support new features powered by live robot data and fleet-scale infrastructure.
- Continuously improve fleet reliability, observability, uptime across both device and cloud systems.
Preferred Qualifications:
- 3+ years of relevant industry experience.
- Strong production programming experience in C++ and Go.
- Prior experience deploying, operating, or managing connected hardware, IoT devices, or robotic systems at scale.
- Experience architecting and managing cloud infrastructure, including services such as AWS, GCP IoT Core, EC2, S3, Lambda, or related technologies.
- Deep understanding of networking protocols commonly used in robotics, IoT, or constrained environments, such as MQTT, WebSockets, gRPC, or similar protocols.
- Demonstrated experience building or managing OTA update systems for edge devices or deployed fleets.
- Strong systems-level understanding of edge computing constraints, including CPU, memory, storage, power cycles, connectivity loss, and local data persistence.
- Experience building fleet observability systems, including telemetry, metrics, logging, alerting, monitoring, and uptime tracking.
- Strong focus on reliability, fault tolerance, self-healing infrastructure, and safe operation of deployed systems.
- Experience with OS-level OTA infrastructure or full-device update mechanisms
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About Skild AI
General-purpose AI for roboticsSkild AI develops general-purpose robotic intelligence for machines operating in the physical world.
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