Senior DevOps Engineer

Position Overview

We are looking for a DevOps Engineer to design, build, and maintain the infrastructure that powers Skild AI’s robotics development at scale. You will own the systems that let our engineering teams move fast — from build tooling and CI/CD pipelines to cross-platform hardware-in-the-loop testing infrastructure. This is a foundational role: you will be setting up and scaling critical systems in a company that is growing quickly, working directly alongside machine learning, robotics software, and hardware engineers to keep our development cycles fast and our deployments reliable.

Responsibilities

• Own and scale Bazel/Skylark build infrastructure from scratch, including custom rules, toolchains, and remote caching across a multi-language codebase.
• Build and maintain CI/CD pipelines for automated software and hardware-in-the-loop (SIL/HIL) testing using Jenkins or equivalent.
• Develop cross-compilation toolchains and system library packages targeting ARM/aarch64 embedded Linux platforms and integrate them into the build system.
• Manage cloud and on-premise infrastructure (AWS/GCP/Azure) supporting model training, simulation, and deployment.
• Maintain infrastructure-as-code (Terraform, Ansible) and own developer environment tooling to accelerate engineering iteration.
• Collaborate with ML, robotics software, and hardware engineers to resolve infrastructure bottlenecks and define tooling roadmaps.

Preferred Qualifications

• BS, MS, or higher in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 5+ years in DevOps, infrastructure engineering, or build/release engineering.
• Hands-on experience owning a Bazel/Skylark build system from scratch at a 100–500 person company, including custom rules, platforms, and toolchain configuration.
• Experience packaging system libraries for cross-compilation to ARM/aarch64 embedded Linux platforms.
• Proven track record building automated SIL/HIL test pipelines (Jenkins or equivalent) for robotics or embedded systems.
• Strong Python and Bash scripting; solid Linux internals, Docker, and cross-compilation knowledge.