Senior Machine Learning Engineer, End-to-End Planning

About Lucid

At Lucid, we are creating exceptional mobility experiences through innovation to drive the world forward. Built on Lucid’s proprietary technology and software-defined vehicle architecture, our award-winning vehicles bring our “Compromise Nothing™” approach to the global automotive market. That means refusing to choose between performance and sustainability, design and engineering, ambition and integrity. In Lucid Air and Lucid Gravity, we have designed and built vehicles that have redefined their segments, combining exceptional range, performance, design, and expansive space in a single experience.

We achieve this through deep vertical integration, with design, engineering, and production happening in-house across our global offices and manufacturing facilities. Our teams come from industries around the world, united by a shared commitment to excellence. By refusing to settle, you can help redefine what’s possible and shape the future of mobility.

Senior Machine Learning Engineer, End-to-End Planning

Role:

• Develop and maintain high-performance, low-latency end-to-end planning algorithms that enable vehicles to safely, accurately, and reliably navigate highway, urban, and parking scenarios in production autonomous driving applications.
• Design reusable and optimized behavior and motion planning software architectures for Level 2/3/4 autonomous driving systems.
• Develop and implement advanced machine learning algorithms for data-driven driving behavior and end-to-end planning.
• Lead software development by applying full-stack autonomy expertise to design and deploy algorithms in closed-loop pipelines and real-world vehicle systems.
• Collaborate closely with cross-functional teams to ensure seamless, robust implementation and deployment of motion planning products for autonomous driving systems.

Required Qualifications:

• Master’s degree or Ph.D. in Computer Science, Robotics, Machine Learning, or a related field.
• Professional experience implementing reliable production systems involving vision-action models, vision-language-action (VLA) models, generative models such as diffusion or flow-matching models, and autonomous driving performance metrics.
• Expert-level knowledge of hands-on experience with Python and PyTorch.
• Solid understanding of state-of-the-art models and approaches in perception, prediction, and planning for autonomous driving.
• Strong foundation in physics, probability, and statistics.
• Experience leveraging AI-assisted development tools to improve software engineering efficiency, code quality, and development workflows.

Preferred Qualifications:

• Experience building and validating real-time autonomous driving or robotics systems, including planning and behavior prediction.
• Proficiency in C++ with strong software design and engineering skills.

Compensation & Benefits: Lucid offers a comprehensive and competitive benefits package including medical, dental, and vision insurance; life and disability coverage; paid time off; paid holidays, paid sick leave; and a 401(k) retirement plan. Hourly/non-exempt employees accrue up to 120 hours paid time off, and salaried/exempt employee accrue up to 160 hours paid time off. Eligible employees may also participate in Lucid’s equity program and/or a discretionary annual cash incentive program. Incentive and equity awards, if applicable, are determined based on individual performance, role scope, market considerations, and overall company results, in accordance with the terms of the applicable plans.

Equal Opportunity: At Lucid, we believe diversity strengthens everything we build. Lucid Motors is proud to be an equal opportunity employer and is committed to providing an inclusive workplace for all. We consider all qualified applicants without regard to race, color, national or ethnic origin, age, religion, disability, sexual orientation, gender, gender identity or expression, marital status, or any other characteristic protected by applicable state or federal laws and regulations.

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