Software Engineer (Onboard Integration)

Join Glydways, a pioneering company in the autonomous vehicle industry. As a Software Engineer on the Onboard Integration team, you will play a crucial role in building the software foundation that runs on every Glydways vehicle. Your work will involve designing, building, and testing mission-critical onboard software, developing high-performance interfaces to onboard devices, and collaborating with various teams to define interfaces and ship features to the fleet. You will have the opportunity to take ownership of features end-to-end and shape the technical direction for the areas you own.

• Design, build, and test mission-critical onboard software that runs on every Glydways vehicle, from prototype to deployment on track.
• Own high-performance interfaces to onboard devices—from cameras, radar, LiDAR, and IMUs to drive-by-wire, controls, and other vehicle systems.
• Collaborate with Autonomy, Hardware, and Operations teams to define interfaces, build evaluation pipelines on real and simulated vehicles, and ship features to the fleet.
• Solid understanding of communication protocols, from low-level (SPI, UART, CAN) to higher-level networking (TCP/UDP)
• Ability to own features end-to-end: clarifying requirements, designing, implementing, testing, and supporting them in the field
• 4+ years of professional software engineering experience, including shipping and supporting production systems
• Strong proficiency in C++ or C for production systems—comfortable navigating and improving an existing codebase as well as writing new components
• Experience with Linux, especially embedded environments (e.g., Yocto, Buildroot, or similar)
• Experience developing device drivers or low-level interfaces for sensors and/or actuators, including use of hardware-in-the-loop (HIL) or similar test frameworks
• Experience developing on resource-constrained embedded hardware (CPU, memory, storage, or bandwidth limited)
• Experience with robotics middleware such as ARK, LCM, ROS, or ROS 2
• Familiarity with ML inference runtimes and integrating ML models into production or edge systems (training experience is a plus, not required)
• Contributions to open-source AI, robotics, or embedded frameworks
• Experience with automotive or other real-time, safety-critical systems
• Prior work in autonomous vehicles, robotics, or complex mechatronic systems
• Experience with performance engineering on embedded CPU/GPU and hardware accelerators
• Experience collaborating directly with operations and field teams to debug and improve systems running in production environments
• If you're excited about this work but don't check every box, we'd still love to hear from you. We know the best candidates don't always match every line of a job description