Meet the team:

The Onboard Integration team builds the software foundation that runs on every Glydways vehicle. We sit within the Embedded Platform & Integration group and partner daily with Vehicle Hardware, Autonomy, Safety, and Track Operations to turn components into a cohesive system on the road.

From streaming data from new sensors, controlling vehicle actuators, and building our custom embedded Linux OS, to integrating ML frameworks and writing tools for live track operations, we own the glue that keeps vehicles and teams moving forward. We move quickly from design to code running on real hardware and value engineers who can dive into ambiguity, find signal in noisy systems, and ship.

 

Roles & Responsibilities:

• Design, build, and test mission-critical onboard software that runs on every Glydways vehicle.
• Own high-performance interfaces to onboard devices—from cameras, radar, LiDAR, and IMUs to drive-by-wire and other vehicle systems.
• Develop and integrate runtimes and middleware so autonomy and ML models can execute and communicate reliably onboard.
• Design and improve systems for live track operations: software deployment, vehicle provisioning, logging, and observability.
• Collaborate with Autonomy, Hardware, and Operations teams to define interfaces, debug complex issues, and ship features to vehicles.
• Contribute to technical design discussions, code reviews, and continuous improvement of our onboard software stack.
• Work independently on projects, seeking guidance on direction and trade-offs while driving execution day-to-day.

 

Knowledge, Skills and Abilities:

• Strong proficiency in C++ or C, with experience shipping production code in embedded, robotics, or similar systems.
• Experience with driver development for sensors and/or actuators, and using test frameworks with Hardware-in-the-Loop.
• Experience developing on resource-constrained embedded hardware.
• Strong knowledge of communication protocols, from low level (SPI, UART, CAN) to higher level (TCP/UDP).
• Experience with Linux, especially embedded environments (e.g., Yocto, Buildroot, or similar).
• Experience with robotics middleware such as ARK, LCM, ROS, or ROS2.
• Ability to take ownership of a problem, break it down, and drive it to completion with moderate guidance.

Preferred Qualifications:

• Familiarity with ML inference runtimes and integrating ML models into production 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.
• Experience with performance engineering on embedded CPU/GPU and hardware accelerators.

 

Ideal Candidate Profile:

• Comfortable working close to hardware and debugging across software, firmware, and electrical boundaries.
• Pragmatic and hands‑on: you like to get from whiteboard to working code on a vehicle quickly.
• Able to work independently on well‑defined goals, asking for guidance where needed but owning execution.
• Collaborative and communicative, especially when working across teams on ambiguous integration problems.

 

This is a hybrid position, 2-3 days/week onsite at the Pittsburgh, PA office.