Senior Research Engineer, Controls

As a Research Engineer, you will deliver mission-critical improvements and new features for our autonomy motion planning and control stack. You will be a crucial part of our team, working alongside engineers, research scientists, and domain experts to build optimal and data driven controls to realize planned vehicle trajectories.

Your responsibilities will include the development of machine-learning vehicle models and learning based control policies leveraging the extensive data we collect every day across our autonomous trucking fleet. You will also have the opportunity to solve real-world autonomy system challenges by participating in vehicle performance analysis, tuning, and troubleshooting. You will contribute significantly to our commitment to pushing the frontiers of technological innovation in Autonomy.

Responsibilities:

• Design, implement, and enhance control algorithms by developing frameworks that integrate MPC with learning based approaches (DL/RL/IL)
• Work cross-functionally with domain experts to implement data driven controller design for scalability
• Develop tools and infrastructure for dataset generation, training, and evaluation to drive advancements in online control optimization
• Ensure all model development keeps a real-time focus and operates efficiently in compute-constrained environments
• Take a lead role in the planning and execution of vehicle testing in the offline simulation environment and on the public road to systematically improve performance, as well as performing root cause analysis and debugging to address the issues
• Track and incorporate the latest research advancements

Required Skills:

• Master's or PhD degree in Computer Science, Mechanical Engineering, Robotics, Aerospace Engineering or related field
• 2+ years of MLE experience or industry experience designing and developing for robotics applications
• Strong foundation in motion control and modern neural network architectures, with expertise in at least one application area, such as IL/RL, time-series analysis, or dynamic system modeling
• Skilled in debugging robotic systems within Linux environments, with strong programming expertise in Python and C++
• Experience model development & training with modern frameworks (e.g. PyTorch)
• Hands-on familiarity with data ingestion and processing pipelines

Preferred Skills:

• Hands-on application skills in any of the following areas: adaptive and nonlinear control, MPC & optimal control, robust control, data-driven control, Kalman filters, etc.
• Have a solid understanding of AV control, vehicle dynamics and drive-by-wire systems
• Proven expertise with application, verification and validation for ADAS/autonomous driving features and functions