Robotics Technologist III: Stochastic Planning and Control for Field Robotics
About The Position
JPL is seeking a Robotics Technologist III with advanced skills in motion control, planning, state estimation, and machine learning, as they apply to robotic systems for the Robotic Surface Mobility Group. This role is part of the Robotic Mobility and Manipulation Section, which develops and matures robotics technology for in-situ exploration of the solar system and provides robotics expertise for space flight hardware, software implementation, and mission operations support. The candidate will work with an enthusiastic multi-disciplinary workforce on robotics technology development and space flight projects, focusing on complex motion control, planning, and state estimation challenges. Responsibilities include developing controls and planning algorithms, integrating deep learning models, and designing high-level multi-agent planning frameworks for robotic operations.
Requirements
- Bachelor’s degree in Computer Science, Mechatronics Engineering, or a related discipline with a minimum of 6 years of relevant experience; a Master’s degree in a related discipline with a minimum of 4 years of relevant experience; or Ph.D. in a related discipline with a minimum of 2 years of relevant experience.
- Experience developing uncertainty-aware motion planning and control systems.
- Demonstrated expertise in estimation theory, simultaneous localization and mapping, and sensor fusion.
- Proficiency in multi-agent planning and POMDP decision-making frameworks.
- Strong programming proficiency in Python, C, and C++.
- Experience developing and operating robot software stacks using ROS2.
- Experience operating and integrating hardware for robotic field expeditions.
- Strong communications skills and ability to work across teams.
Nice To Haves
- Familiarity with simulation tools for robotic development such as Gazebo, IsaacSim, or MuJoCo.
- Proficiency with modern machine learning frameworks (e.g., PyTorch, TensorFlow) for training and deploying perception and vision models.
- Experience with compute optimization and GPU acceleration (e.g., CUDA, TensorRT) for real-time, onboard robotic processing.
- Familiarity with diverse sensing modalities (e.g., LiDAR, stereo cameras, IMUs) tailored for resource-constrained or GPS-denied environments.
- Experience in continuous integration and rigorous software testing methodologies (e.g., hardware-in-the-loop, software-in-the-loop) for autonomous operations.
Responsibilities
- Design and implement constraint-aware motion planning and controls for multi-agent systems.
- Apply deep learning for perception-aware stochastic planning.
- Develop low-level algorithms utilizing model predictive control, reinforcement learning, and grid-based or sampling-based planning.
- Develop higher-level planning frameworks incorporating Partially Observable Markov Decision Processes (POMDP) and action selection using Monte Carlo Tree Search.
- Perform robust robot system integration work with the existing ROS2-based robot software stacks.
- Support field trials to evaluate autonomous navigation of robotic systems.
Benefits
- Variety of health, dental, vision, wellbeing, and retirement plans
- Paid time off
- Learning
- Rideshare
- Childcare
- Flexible schedule
- Parental leave
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree
