What you'll do
- Develop real-time (deterministic, randomized, and optimization-based) path and motion planning algorithms for various vehicle types (Ackermann, skid-steered, wheeled, tracked) using C++ in a Linux environment
- Integrate planning algorithms with on-vehicle systems, including perception, localization, controls, and platform interfaces
- Deploy, test, and debug autonomy software directly on vehicles, addressing real-time constraints, system latency, and hardware limitations
- Design and implement messaging for synchronization, logging and debugging across distributed systems
- Own end-to-end validation: simulation → HIL → on-vehicle testing, ensuring consistency and performance across environments
- Diagnose and resolve issues observed in field testing, including edge cases arising from sensing, actuation, and environment variability
- Work closely with cross-functional teams (Perception, Controls, Platform, Systems Engineering) to ensure robust and reliable vehicle behavior
- Travel up to 20% to support on-site vehicle integration and testing
Qualifications
- BS, MS, or PhD in Robotics, Applied Mathematics, Mechanical Engineering, Computer Science, or related field
- Strong background in path planning, motion planning, or related autonomy domains
- Strong programming skills in C++
- Solid software engineering fundamentals: system design, unit/integration testing, debugging
- Experience deploying or integrating software on physical robotic or automotive platforms
- Ability to deliver production-quality software in a continuously integrated environment
- Demonstrates clean, maintainable code and documentation practices
- Strong problem-solving skills with a proactive, hands-on approach
Preferred Qualifications
- Prior experience with unmanned ground vehicles operating in outdoor environments
- Experience with on-vehicle debugging, telemetry analysis, and real-time system profiling
- Familiarity with robotics middleware (e.g., ROS/ROS2 or similar frameworks)
- Experience working across perception–planning–controls interfaces
- Expertise with GPU or ML toolkits such as CUDA, PyTorch, TensorFlow, and/or TensorRT
Compensation
US Salary Range: $150,000—$180,000
Equity included. Competitive benefits: premium healthcare (80% covered), life/disability insurance, PTO (20 days), parental leave (7 weeks), tuition reimbursement ($9k), 401(k) with 4% match.