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Applied Intuition is a $15B-valued Silicon Valley company (founded 2017) powering physical AI for autonomous vehicles and mobile robots. The company serves automotive, defense, trucking, construction, mining, and agriculture industries with digital infrastructure, operating systems, and autonomy solutions. Eighteen of the top 20 global automakers and the US military trust Applied Intuition's platform.
You will join the controls engineering team to design and implement controls modules for autonomous off-road vehicle navigation. Your responsibilities include:
- Design and implement controls modules deployable on off-road vehicles for autonomous navigation
- Characterize vehicle dynamics to create tailored controls solutions and reduce simulation-to-reality gaps
- Evaluate and apply academic research to develop optimal-control solutions using techniques like nonlinear MPC and MPPI control
- Collaborate with customers' engineers and manage customer relationships
The role is hands-on and fast-paced, requiring you to tackle challenging control problems across diverse vehicle types in difficult off-road environments (steep slopes, sandy/muddy surfaces). Modules must meet high-performance standards due to critical timing, thermal, and power constraints.
Applied Intuition is headquartered in Sunnyvale with offices globally (Washington DC, San Diego, Ft. Walton Beach, Ann Arbor, London, Stuttgart, Munich, Stockholm, Bangalore, Seoul, Tokyo). The company operates primarily in-office (5 days/week), with flexibility for occasional remote work and schedule adjustments.
REQUIREMENTS:
- 3+ years of experience building production software using modern software practices
- Hands-on experience testing controls solutions on real, production-grade vehicles
- Fluency in differential equations, numerical optimization, numerical analysis, and physics
- Passion for tackling challenging control problems in off-road autonomous environments
- Enthusiasm for practical solutions to cutting-edge challenges in fast-paced environments
NICE TO HAVE:
- MSc or PhD in mechanical engineering or computer science with controls coursework/research
- Experience with safety-critical C++ code compliance (MISRA, AUTOSAR)
- Experience with quadratic programming solvers and nonlinear optimization tools (OSQP, SCS, CasADi)
- Machine learning applied to vehicle dynamics modeling
- High-performance C++ development
- Experience with large industrial vehicles in off-road environments
