Senior Controls Engineer – Autonomy

The Controls Team is responsible for the synthesis and execution of dynamic trajectories by the Motional Autonomous Vehicle. This team focuses on the generation of comfortable and safe trajectories for the AV to perform, the design of control systems that interface with vehicle platform actuators, and the communication interfaces that relay trajectory information to low-level control modules. The team pursues these goals through a process of rigorous algorithmic design, validation, analysis in simulation, and comprehensive closed-course and public road evaluation.

We are looking for a Senior Controls Engineer to help build a robust motion planning and control pipeline. This person will work on enabling cutting-edge capabilities for Motional’s AV, including algorithm design and documentation, software development, metrics definition, simulation implementation and evaluation, and in-vehicle testing. In the immediate term, we are expecting the candidate to support the development of the reverse driving feature of the AV from scratch. As part of this role, the team member will maintain cross-functional relationships across the organization to help understand and communicate the impacts of their work on overall AV performance.

What you will work on:

• Bringup of a reverse driving controller (longitudinal and lateral) from scratch to support reverse driving capabilities across Motional’s fleet of AVs.
• Design, implement, and test low-level controllers, state estimators, and state-machine logic that directly interface with platform sensors and actuators to produce performant, comfortable, and safe motion tracking.
• Architect and implement dynamic simulation environments that accelerate the design and analysis of motion planning and control algorithms, including validation of simulation models against real-world data.
• Integrate upstream trajectory generation and downstream motion tracking controllers through principled trajectory interface design, including generator / controller state machine logic design and trajectory communication formulation. 
• Develop test/analysis tooling for isolated evaluation of motion tracking performance and incremental integration testing that incorporates upstream components (trajectory generation, planning, perception, etc.), select test parameters, and analyze test results.
• Define and implement performance, comfort, and safety metrics for motion planning and control, and perform quantitative analysis of fleet-wide vehicle data to validate design improvements and identify key areas of improvement.
• Formulate, implement, analyze, and test optimization and machine learning-based frameworks for dynamic trajectory synthesis.
• Build a system-wide understanding of Motional’s autonomy stack components by supporting root-cause analysis of simulation, closed-course, and public road events from a motion planning and control perspective.
• Travel for in-person vehicle testing at Motional’s and our partner's deployment and test track locations, typically once every 4 to 6 months.

Desired Background:

• MS, PhD preferred, in one of Control Theory / Robotics / Computer Science / Applied Math or a related field
• 3+ years of experience developing controls / motion planning related algorithms and components in an industrial setting
• Experienced with concepts from control theory, including linear/nonlinear systems, state estimation / Kalman filtering, model predictive control, data-driven system identification, time-series data processing, and data visualization
• Familiarity with numerical optimization, quadratic/conic programming, convex optimization, nonlinear programming, and non-convex optimization
• 3+ years of industry experience implementing C++/C and Python
• Familiarity with Linux-based operating systems

Bonus Points:

• Proven experience in an industry setting with the implementation of reverse driving trajectory tracking control for vehicles
• Familiarity with vehicle dynamics and modeling, including longitudinal/lateral vehicle dynamics, tire modeling, EV drive-train / steer dynamics, etc.
• General familiarity with machine learning, including supervised learning, reinforcement learning, training/test validation, etc.
• Publications in Controls/Robotics or Autonomous Driving conferences/journals (CDC, ACC, TAC, CSL, Automatica, IROS, ICRA, CoRL, RAL, TRO, IV, ITSC, T-ITS)
• Familiarity with SQL, Looker, MATLAB/SimuLink
• Familiarity with Gitlab, Bazel, VSCode / CLion
• Familiarity with AV industry simulation tooling
• Familiarity with CAN communication protocol, AUTOSAR, AURIX
• Familiarity with real-time kinematic (RTK) GPS localization

The salary range for this role is an estimate based on a wide range of compensation factors inclu