ML Prediction and Planner - Lead

About Applied Intuition

Applied Intuition is the vehicle intelligence company that accelerates the global adoption of safe, AI-driven machines. Founded in 2017 and now valued at $15 billion following its recent Series F funding round, Applied Intuition delivers the Vehicle OS, Self-Driving System, and toolchain to help customers build intelligent vehicles and shorten time to market. 18 of the top 20 global automakers and major programs across the Department of Defense trust Applied Intuition's solutions to deliver vehicle intelligence. Applied Intuition services the automotive, defense, trucking, construction, mining, and agriculture industries and is headquartered in Mountain View, CA, with offices in Washington, D.C., San Diego, CA, Ft. Walton Beach, FL, Ann Arbor, MI, 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. (Note : For EpiSci job openings, fully remote work will be considered by exception.)

About the role

We are looking for a lead software engineer with expertise in ML-first prediction and planning for autonomous vehicles or mobile robots. You will lead the engineering team in prototyping, evaluating, refining, and deploying state-of-the-art ML capabilities for our autonomous vehicle stack.

At Applied Intuition, you will :

• Lead the development of machine learning models for joint behavior prediction and planning, with the goal of end-to-end differentiable autonomy
• Architect integrated ML systems that reason over latent scene representations from perception to directly inform or generate trajectory decisions
• Implement data-driven approaches that optimize the full autonomy stack jointly
• Collaborate tightly with Perception, Mapping, and Simulation teams to design shared representations and scalable training loops, including imitation and reinforcement learning
• Mentor a team of ML engineers focused on reasoning, joint prediction and planning, and generalization across ODDs, aligned with the trucking roadmap

We're looking for someone who has :

Nice to have :

The salary range for this position is $199,295 USD to $264,500 USD annually. This salary range is an estimate, and the actual salary may vary based on the Company's compensation practices.

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.

Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws.