You'll work at the boundary where algorithms meet hardware, where recorded data meets live systems, and where a research idea becomes software that runs deterministically at 30 frames per second in a moving car.
What you'll do- Own the runtime infrastructure of a real-time vision system: task orchestration, inter-process messaging, sensor ingestion, and deterministic data flow under hard latency budgets
- Build the infrastructure that lets the vision stack run anywhere - on target ECUs, on a developer's desk, and at scale against large volumes of recorded drive data
- Design and evolve data replay, simulation, and regression-testing systems that turn field recordings into fast, repeatable development loops
- Keep the stack portable and efficient across compute platforms and DNN inference runtimes, from datacenter GPUs to automotive SoCs
- Design the real-time communication paths that connect the vision system to vehicle platforms and external consumers
- Engineer to automotive safety and security standards (ISO 26262, ISO 21434) without giving up development speed
- Build and sharpen the team's AI-assisted engineering workflows - coding agents, AI-assisted code review, test generation - and set the standard for how they're used well
What you bring- 6+ years building system software for real-time or embedded environments
- Expert-level C++ (and solid C), including debugging on real hardware under real-time constraints
- Deep grounding in operating systems and computer architecture: scheduling, memory, caches, IPC, and what they cost
- Hands-on ROS or ROS2 experience - designing nodes/components, defining messages, working with bag data
- Fluency in serialization and middleware (Protocol Buffers, DDS, ZeroMQ, or similar) and network programming over TCP/UDP/Ethernet
- Working practice with AI-assisted development tools as a normal part of how you build, debug, and test
- BS/MS in Computer Science or equivalent experience
Nice to have- Depth in an RTOS or safety-grade platform: QNX, RT Linux, or Green Hills INTEGRITY
- Experience across both ROS1 and ROS2 ecosystems, including bag formats (rosbag/rosbag2/MCAP) and DDS configuration
- DNN inference runtimes and accelerators: TensorRT, TIDL, CUDA, quantized inference, and their pre/post-processing pipelines
- Cloud-scale data or simulation pipelines: containers, orchestration, batch compute, CI at fleet-data volume
- Automotive communication protocols: CAN, automotive Ethernet, SOME/IP
- Time spent shipping software in an automotive, robotics, or autonomous-vehicle setting
Benefit- Medical, dental, and vision coverage
- Office snacks & reimbursable meals*
- Paid Time Off
- FSA
- 401K
