Software Engineer, Onboard Integration
$190,000–$220,000 year
HybridRichmond, California, United States
Job Summary
Design, build, and test mission-critical onboard software for Glydways vehicles, ranging from prototype to track deployment. Own high-performance interfaces to onboard devices including cameras, radar, LiDAR, and drive-by-wire systems. Develop runtimes and middleware for autonomy and ML workloads, while designing systems for live track operations such as deployment, provisioning, and observability. Collaborate with Autonomy, Hardware, and Operations teams to define interfaces, build evaluation pipelines, and ship features to the fleet. Investigate and debug issues on the bench and on-vehicle to drive root cause analysis and stable fixes. Shape technical direction through design discussions and code reviews. This role requires 4+ years of C++ or C experience, proficiency in embedded Linux, and robotics middleware like ROS 2. The position is hybrid, with 2-3 days onsite in Richmond, CA, and offers a salary range of $190,000-$220,000 USD plus stock options.
Required Qualifications
- 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 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)
- Solid understanding of communication protocols, from low-level (SPI, UART, CAN) to higher-level networking (TCP/UDP)
- Experience with Linux, especially embedded environments (e.g., Yocto, Buildroot, or similar)
- Experience with robotics middleware such as ARK, LCM, ROS, or ROS 2
- Ability to own features end-to-end: clarifying requirements, designing, implementing, testing, and supporting them in the field
- *This is a hybrid position, 2-3 days/week onsite in Richmond, CA. No relocation assistance will be provided.
Desired Qualifications
- 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
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