Senior Software Engineer (Manipulation)

General Motors•Mountain View, CA
•$129,400 - $234,100•Remote

About The Position

General Motors is reimagining automation at economy scale to make U.S. manufacturing more competitive. This Senior Software Engineer role is part of the core robotics team and works alongside controls and perception engineers to build manipulation planning and control libraries. The work spans collision-free motion generation, trajectory optimization, and the execution layer that converts planned motion into repeatable movement on physical arms. Target applications use C++, Python, and ROS 2 with FANUC arms and mobile manipulators, subject to cycle-time and reliability requirements. The role owns manipulation planning capabilities, including collision-free planning, trajectory optimization, time parameterization, Cartesian and constrained planning for approach, grasp, and place, and mobile manipulation planning. It also develops collision environment representations from perception output and static scene models, and builds an execution layer that monitors trajectory tracking and responds to deviations and contact. The engineer defines interfaces among perception, planning, and control, diagnoses failures using field data, and turns failures into regression cases. The position is remote, with an expectation to report to a GM hub three times a week or at another frequency set by the manager if the selected candidate lives within a specified radius of a hub. Relocation benefits are available to qualifying candidates under company policy.

Requirements

  • At least 6 years of professional engineering experience, including at least 2 years in motion planning or manipulation.
  • Experience deploying manipulation planning on physical arms in a production or field setting.
  • Experience meeting cycle-time requirements, not only feasibility requirements.
  • M.S. degree or higher in Robotics, Computer Science, Mechanical Engineering, or a closely related technical field; equivalent industry experience with demonstrated simulation depth may be considered.
  • Strong grounding in motion planning, including sampling-based and optimization-based methods and their failure modes.
  • Solid knowledge of kinematics and dynamics, including forward and inverse kinematics, redundancy resolution, singularity handling, and Jacobian methods.
  • Experience with trajectory optimization and time-optimal path parameterization.
  • Experience with collision checking and distance queries, including their performance characteristics.
  • Hands-on experience with MoveIt, OMPL, TrajOpt, Drake, or comparable frameworks.
  • Strong C++ and Python skills.
  • Hands-on experience with ROS or ROS 2.
  • Ability to judge how much fidelity a validation question requires and to trade simulation speed against accuracy.
  • Ability to work effectively with remote teams across multiple time zones.
  • Treats simulation as a product with internal users.

Nice To Haves

  • Experience with FANUC arms or FANUC controller interfaces.
  • Experience with force control, impedance control, or contact-rich manipulation.
  • Experience with mobile manipulation and whole-body control.
  • Experience with learned or hybrid planning approaches.
  • Prior open-source contributions to MoveIt or the ROS manipulation ecosystem.

Responsibilities

  • Build and own manipulation planning capabilities, including collision-free planning, trajectory optimization, and time parameterization.
  • Implement and tune planners for target applications, treating cycle time as a first-class requirement.
  • Build collision environment representations from perception output and static scene models.
  • Implement Cartesian and constrained motion planning for approach, grasp, and place phases.
  • Handle mobile manipulation planning, including base placement, redundancy resolution, and whole-body motion.
  • Build an execution layer that monitors trajectory tracking and responds to deviations and contact.
  • Define and maintain clean interfaces between perception, planning, and control as the system grows.
  • Diagnose planning failures using field data and turn them into regression cases.

Benefits

  • Medical, dental, and vision benefit options.
  • Health Savings Account and Flexible Spending Accounts.
  • Retirement savings plan.
  • Sickness and accident benefits.
  • Life insurance.
  • Paid vacation and holidays.
  • Tuition assistance programs.
  • Employee assistance program.
  • GM vehicle discounts.
  • Incentive pay program with payouts based on company performance, job level, and individual performance.
  • Relocation benefits for candidates who qualify under company policy.
© 2026 Teal Labs, Inc
Privacy PolicyTerms of Service