Applied Intuition is looking for a Motion Planning Engineer to design and implement cost and reward functions for their autonomous driving system, bridging the gap between the core planning team and RL research experts to directly influence vehicle behavior and accelerate the adoption of safe, AI-driven machines.
Requirements
- Strong programming skills in C++ and/or Python
- Solid understanding of core motion planning concepts, including state-space search, sampling-based methods, and trajectory optimization
- Demonstrated ability to analyze complex system behavior and distill it into clear, quantitative metrics
- Prior exposure to machine learning concepts, particularly reinforcement learning
- Experience with robotics middleware (e.g., ROS)
- Experience with a simulation environment
Responsibilities
- Design, implement, and validate novel reward and cost functions that guide the autonomous vehicle's behavior, balancing safety, comfort, and efficiency
- Work with our behavior team to integrate and optimize cost functions, ensuring optimal trajectory generation and comfortable, safe driving
- Partner with our Reinforcement Learning (RL) research team to translate high-level behavioral goals into effective RL reward signals for training our learning-based agents
- Analyze planning system performance against real-world and simulated data to iteratively refine and improve cost and reward mechanisms
- Take ownership of the full life cycle of reward engineering solutions, from concept and prototyping to deployment and maintenance
Other
- 3-5 years of professional experience in autonomous vehicle motion planning, robotics, or a related field
- Enthusiasm about applying their skills to solve challenging problems in a fast-paced environment
- We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week.
- Base salary range for this full-time position in the location listed is: $125,000 - $222,000 USD annually.
