Sr. Behavior Planning and Prediction Software Engineer - ADAS

The main responsibilities of the Sr. Behavior Planning and Prediction Software Engineer at Lucid include designing, developing, and evaluating advanced machine learning architectures for behavior planning in ADAS systems. You'll conduct research to integrate the latest innovations into our autonomous driving models while managing data curation and transport from our fleet to improve our decision-making capabilities.

To qualify for the Sr. Behavior Planning and Prediction Software Engineer role at Lucid, candidates should possess proficiency in C++ and Python with a minimum of three years of relevant experience. A deep understanding of machine learning principles, software design, and data management is crucial, along with academic credentials in Computer Engineering, Computer Science, or Data Science.

The base pay range for the Sr. Behavior Planning and Prediction Software Engineer at Lucid is between $175,900 and $257,950 annually. Compensation is influenced by various factors including knowledge, skills, experience, education, and training, ensuring a fair and competitive salary tailored to individual qualifications.

At Lucid, the work environment for a Sr. Behavior Planning and Prediction Software Engineer is dynamic and collaborative, filled with like-minded innovators dedicated to making a significant impact in the electric vehicle industry. You'll be encouraged to exchange ideas, push boundaries, and tackle challenges in a supportive atmosphere where excellence is the norm.

Lucid supports the professional growth of a Sr. Behavior Planning and Prediction Software Engineer through continuous learning opportunities, access to cutting-edge technology, and collaboration with industry experts. With a focus on innovation, you will be encouraged to engage in research and development activities that contribute to your skills and career advancement.

When answering this question, highlight your specific contributions, the technologies used, and the outcomes of the project. Emphasize your role in integrating machine learning into relevant systems, challenges faced, and how you addressed them.

Discuss your systematic approach to data analysis, including tools and techniques you've implemented. Explain how your analysis has directly contributed to enhancing decision-making in autonomous driving systems.

Address this by outlining best practices in software design, your familiarity with efficient algorithms, and how you prioritize scalability in your development process. Provide examples of past successes.

Draw from your professional background to explain the deep learning principles you've applied. Discuss specific projects, models developed, and areas like reinforcement learning or imitation learning where you have practical experience.

Articulate your methods for keeping informed, like following relevant journals, attending conferences, and participating in online forums or communities. Highlight how this knowledge aids your work as a software engineer.

Provide a concise narrative of a specific challenge related to machine learning integration, the steps you took to resolve it, and the outcomes. Emphasize your problem-solving skills and adaptability.

Discuss your understanding and exposure to alternative planning methods in your work. Emphasize their application within Autonomous Driving contexts and how they complement machine learning approaches.

List the tools and libraries you're proficient in, explaining why you favor them. Include any specific use cases that demonstrate your expertise and the impact they had on your previous projects.

Share your philosophy on teamwork, touch on communication styles, and provide examples of successful collaborations that led to impactful project results. Emphasize your contributions to fostering a positive team environment.

Convey your passion for innovation and how it drives you to contribute to sustainable mobility solutions. Discuss how you see your role impacting the industry and future technologies.