Senior DevOps Engineer, Robotics

Knightscope•Sunnyvale, CA

3d•Onsite

About The Position

Knightscope is seeking a Senior DevOps Engineer to manage the build, deployment, and release infrastructure for its autonomous security robot fleet. This role involves working within a small, efficient team to oversee CI/CD pipelines, cloud infrastructure, and Over-The-Air (OTA) deployment systems for robots operating 24/7 across the US. The engineer will be responsible for the infrastructure that supports autonomy, security perception, and embedded firmware for the robotics platform.

Requirements

10+ years in DevOps, SRE, or platform engineering with ownership of CI/CD systems, cloud infrastructure, and production deployments.
Hands-on experience with GitHub Actions or GitLab CI, Git-based workflows, and collaborative software development at scale.
Strong Linux systems expertise including networking, storage, performance, and security.
Practical experience with AWS and container technologies including Docker and Kubernetes.
Experience supporting C++ and Python development teams on Linux.
Python scripting and automation tooling experience.

Nice To Haves

Experience with ROS2-based robotics development workflows, multi-repository builds, and platform-specific targets.
Experience with multi-architecture CI environments spanning x86 and ARM64 embedded targets.
Experience with OTA update systems or fleet deployment pipelines for embedded or edge hardware.
Background in ML model deployment pipelines: model packaging, registry management, and staged rollout to edge devices.
Previous experience in release engineering including branching strategies, artifact management, and CDN distribution.

Responsibilities

Build, operate, and continuously improve end-to-end CI/CD pipelines for a production robotics platform spanning autonomy, security perception, and embedded firmware stacks.
Own the OTA update pipeline: multi-image container builds, staged fleet rollout, and rollback across heterogeneous hardware targets.
Manage cloud infrastructure and Kubernetes deployments supporting ML training pipelines, model registry, and fleet telemetry.
Triage CI failures, distinguish infrastructure from product issues, and drive resolution with clear ownership.
Improve observability across pipelines and deployed systems through automation, metrics, logging, and monitoring.
Build and maintain internal tooling, leveraging AI to automate workflows, reduce toil, and improve CI failure analysis.
Mentor engineers and contribute to platform architecture decisions across the team.