Software Engineer, Applied Intuition

• 4+ years of experience in motion planning for autonomous vehicles or robotics
• Strong foundation in robotic motion planning algorithms and trajectory generation (optimization-, search-, or rule-based)
• Experience building deterministic, safety-critical planning systems
• A data-driven mindset for large-scale evaluation, debugging, and tuning of planning behavior
• Proficiency in C++ and experience working in real-time systems
• Strong systems thinking and cross-functional collaboration skills
• Bachelors degree in Computer Science, Computer Engineering, Electrical Engineering, or related field
• Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles

Desirable

• As a Motion Planning Engineer on the Fallback Stack team, you will design and ship deterministic, safety-critical planning systems that ensure autonomous vehicles behave safely when autonomy degrades or operates under uncertainty
• This role emphasizes classical motion planning, predictable behavior, and large-scale evaluation, rather than deep learning–driven planning
• In addition to your engineering contributions, by working in our dynamic and customer-focused team culture, you will contribute to and learn from best practices in the nascent autonomy industry
• We move fast and we focus on excellence, for our products and for our business. If you are hands-on and looking for a place to have a multiplying effect on making autonomous systems a reality, Applied is the place for you!
• Design and implement classical or ML motion planners for fallback and minimal-risk maneuvers
• Build planners that operate reliably under degraded perception, partial observability, and system faults
• Define and execute safe, deterministic vehicle motions such as controlled slow-downs, pull-overs, and safe stops
• Use large-scale simulation and real-world data to evaluate planner behavior and guide parameter tuning
• Develop metrics, analysis tools, and dashboards to understand planner performance at scale
• Collaborate closely with behavior prediction, perception, controls, safety, and remote assistance teams
• Contribute to a reusable fallback platform used across trucking and other autonomy programs