Software Engineer - Motion Planning (Fallback Stack)
Applied IntuitionSunnyvale, California, United StatesPosted 26 March 2026
Job Description
About Applied Intuition
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co .
We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments.
About the role
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!
At Applied Intuition, you will:
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
We're looking for someone who has:
5+ 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
Nice to have:
Experience designing minimal-risk maneuvers (MRM) or emergency handling behaviors
Familiarity with AV safety concepts, ODD constraints, or safety-case-driven development
Experience using ML techniques for parameter tuning, calibration, or offline optimization
Experience working with degraded sensors, uncertainty, or human-in-the-loop systemsBackground in simulation frameworks or large-scale log analysis
Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also inclu ... (truncated, view full listing at source)
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