Software Engineer - Planning (Reward Design)

Applied Intuition•Sunnyvale, CA

2d•Onsite

About The Position

We are looking for a Motion Planning Engineer with a specialized focus on designing and implementing cost and reward functions for our autonomous driving system. This role requires a strong background in classical motion planning and a keen interest in how behavioral objectives can be codified into quantifiable metrics for both traditional and machine learning-based control systems. You will be the bridge between our core planning team and our RL research experts, directly influencing the vehicle's behavior. We operate in a dynamic and customer-focused culture that moves fast and prioritizes excellence in our products and our business. This is a place for hands-on engineers who want to tackle the complex, real-world challenges of making autonomous systems a reality. You will have a direct and multiplying effect on the performance of our autonomous vehicles while contributing to and learning from the best practices of a nascent industry. If this sounds like the impact you want to make, Applied Intuition is the place for you!

Requirements

3-5 years of professional experience in autonomous vehicle motion planning, robotics, or a related field
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
Enthusiasm about applying their skills to solve challenging problems in a fast-paced environment, and building towards an end-to-end strategy

Nice To Haves

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