Senior Software Engineer (Manipulation)

GM•Mountain View, CA
•Remote

About The Position

General Motors is reimagining automation at economy scale to make manufacturing more competitive in the USA. The Senior Software Engineer will build manipulation planning and control libraries for C++, Python, and ROS 2 applications on FANUC arms and mobile manipulators, with cycle time and reliability as key requirements. This role is part of the core robotics team and works alongside controls and perception engineers. The engineer will build and own manipulation planning capability, including collision-free motion planning, trajectory optimization, time parameterization, and the execution layer that turns planned motions into repeatable movement on real arms. The role includes building collision-environment representations from perception output and static scene models; implementing Cartesian and constrained planning for approach, grasp, and place; and handling mobile manipulation through base placement, redundancy resolution, and whole-body motion. The engineer will define clean interfaces between perception, planning, and control, monitor trajectory tracking and respond to deviation and contact, and use field data to diagnose failures and create regression cases. The position is based remotely. Candidates who live within a specified radius of a GM hub will be expected to report to that location three times a week or at another frequency set by their manager. Relocation benefits are available to candidates who qualify 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 assess the level of fidelity required for a validation question and balance 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 capability, 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.
  • Plan mobile manipulation cases involving base placement, redundancy resolution, and whole-body motion.
  • Build an execution layer that monitors trajectory tracking and responds to deviation and contact.
  • Define and maintain clean interfaces between perception, planning, and control.
  • Diagnose planning failures using field data and turn them into regression cases.

Benefits

  • Medical benefits
  • Dental insurance
  • Vision insurance
  • Health Savings Account
  • 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
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