Senior Engineer - Motion Planning and Controls, Autonomous Driving

 

Senior Software Engineer, Motion Planning, Parking

Leading the future in luxury electric and mobility At Lucid, we set out to introduce the most captivating, luxury electric vehicles that elevate the human experience and transcend the perceived limitations of space, performance, and intelligence. Vehicles that are intuitive, liberating, and designed for the future of mobility. We plan to lead in this new era of luxury electric by returning to the fundamentals of great design – where every decision we make is in service of the individual and environment. Because when you are no longer bound by convention, you are free to define your own experience. Come work alongside some of the most accomplished minds in the industry. We’re providing a community for innovators who want to make an immediate and significant impact. If you are driven to create a better, more sustainable future, then this is the right place for you.

Role Overview

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.

Key Responsibilities:

• Motion Planning & Trajectory Optimization
  • Develop and enhance trajectory optimization algorithms (e.g., iLQR / MPC-based approaches) for low-speed scenarios
  • Design and implement constraint-aware optimization, including
  • Improve local planners to generate control-friendly trajectories
• System Architecture & Performance Optimization
  • 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
• Debugging, Validation, and Production Integration
  • 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
• Research, Prototyping, and Innovation
  • 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
• Cross-Team Collaboration & Knowledge Transfer
  • 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

Required Qualifications:

• 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

Preferred Qualifications:

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

 

 

 

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