Senior Software Engineer, Motion Planning, Parking

Lucid Motors•Newark, CA

5h•$179,400 - $263,120•Onsite

About The Position

We are seeking a Senior Motion Planning Engineer to support the auto-parking and low-speed autonomy stack. This role focuses on trajectory optimization, path smoothing, and local planning, with direct impact on parking features such as Auto Park, Summon, and Park-to-Park. The ideal candidate will combine strong algorithmic foundations with production-grade software execution, driving both system performance and scalability. This role is based in Newark, CA and requires employees to be onsite five days a week.

Requirements

M.S. or Ph.D. in Robotics, Control, Electrical Engineering, or related field
2+ years of software development experience in autonomous driving, ADAS, or robotics, with a focus on large-scale data and real-time systems
Strong programming skills in C/C++ with solid understanding of software engineering design principles and best practices
Proven track record of delivering ADAS/AD features through the full production lifecycle: requirements analysis, architecture/design, implementation, unit testing, feature tuning, SIL/HIL validation, and in-vehicle deployment
Hands-on experience with software integration on embedded platforms (e.g., NVIDIA Xavier/Orin, Infineon Aurix)
Demonstrated ability to build reliable production-grade systems, including large-scale architectures, evaluation pipelines, and performance-optimized models

Nice To Haves

ML-based planning approaches
GPU or parallel computing for planning
Track record of production deployment in real vehicles

Responsibilities

Develop and enhance trajectory optimization algorithms (e.g., iLQR / MPC-based approaches) for low-speed scenarios
Design and implement constraint-aware optimization
Improve local planners to generate control-friendly trajectories
Refactor and scale planning stack architecture for maintainability and extensibility
Optimize CPU usage and explore parallel/GPU acceleration opportunities
Improve system-level behaviors such as: Stability under dynamic constraints, Scalability across scenarios and vehicle platforms
Investigate and resolve real-world issues such as: Curvature discontinuities, velocity/trajectory mismatches, and edge-case steering behavior
Validate solutions using: Simulation and vehicle logs, Multi-scenario testing and benchmarking
Evaluate and prototype new approaches (e.g., learned models, barrier functions, ML-based planning)
Benchmark and compare planning algorithms for production viability
Contribute to next-gen planning initiatives, including ML-driven planning
Drive technical discussions and share algorithm insights across teams
Maintain high-quality documentation and support knowledge transfer
Track work via Jira with clear metrics and reproducible results