Senior Robotics Software Engineer – AI-Enabled Manipulation & Industrial Automation

Job Description

The Senior Robotics Software Engineer – AI-Enabled Manipulation & Industrial Automation designs, develops, integrates, validates, and deploys AI-enabled robotic applications for industrial and factory automation. The role spans robotic manipulation, perception, motion planning, computer vision, simulation, controls, and AI—including vision-language-action (VLA) models and learned manipulation policies. 

The engineer develops end-to-end, closed-loop solutions that combine classical robotics and modern AI for tasks such as perception-guided manipulation, multimodal task understanding, sensor-driven feedback, and autonomous execution. This role bridges robotics software, artificial intelligence, and manufacturing automation, with a focus on turning advanced prototypes into robust, scalable, production-relevant systems. 

Key responsibilities 

• Design and deploy robotic applications for industrial automation, including pick-and-place, material handling, part transfer, machine tending, inspection support, and vision-guided manipulation. 

• Build collision-aware, dynamically feasible manipulation pipelines integrating object detection and pose estimation, grasp generation, inverse kinematics, motion and trajectory planning, collision avoidance, reliable execution, and gripper control using frameworks such as MoveIt, Lula, RMPflow, OMPL, or related technologies. 

• Develop computer-vision and AI perception systems for detection, semantic and instance segmentation, 6D pose estimation, RGB-D and point-cloud processing, scene understanding, and grasp-point generation. 

• Integrate AI/ML models into real-time or near-real-time robotic workflows to improve perception, generalization, manipulation, task-level decision-making, and runtime reliability. 

• Develop closed-loop robotic systems that use sensor feedback to monitor task execution, detect failures, update robot actions, and improve runtime reliability. 

• Develop and evaluate VLA models and learned policies for multimodal task understanding, natural-language instruction following, action generation, imitation learning, and autonomous manipulation. 

• Use NVIDIA Isaac Sim, Isaac Lab, Omniverse, Replicator, and related tools to build digital environments, generate synthetic data, validate behavior, and develop simulation-to-real workflows before physical deployment. 

• Own robotic software architecture across perception, planning, control, simulation, AI inference, data flow, system interfaces, hardware integration, and deployment; develop modular, maintainable software using Python, C++, ROS 2, Linux, Git, Docker, and reproducible development environments. 

• Integrate and calibrate manipulators, grippers, RGB/RGB-D and depth cameras, force/torque sensors, and other automation hardware, including camera intrinsics/extrinsics, hand-eye calibration, and robot-to-cell registration; make calibration robust and repeatable in the field. 

• Implement force and compliant control—such as impedance, admittance, and guarded moves—for contact-rich tasks including insertion and assembly. 

• Build data-collection workflows for learned policies, including teleoperation, demonstration capture, dataset curation, and experiment tracking. 

• Debug and validate systems across software, AI models, perception, simulation, motion planning, controls, networking, and hardware. Define measures such as task success, perception accuracy, cycle time, trajectory quality, repeatability, and reliability. 

• Collaborate with robotics, perception, ML, controls, manufacturing, and plant-facing teams to troubleshoot issues, document solutions, and transition prototypes from simulation through hardware integration, validation, and deployment. 

• Provide technical ownership of robotic applications from early prototyping and simulation through hardware integration, validation, and deployment. 

• Optional: Integrate robotic cells with industrial systems and safety infrastructure, including PLCs (OPC UA, Ethernet/IP, Modbus), safety I/O, and robot safety configurations in accordance with ISO 10218 and ISO/TS 15066. 

Required qualifications 

• Master’s degree in Computer Science, Robotics, Mechanical, Electrical, or Computer Engineering, Artificial Intelligence, Machine Learning, or a related technical field. 

• 2+ years of experience developing robotic software or applications involving manipulation, perception, motion planning, simulation, industrial automation, or related systems. 

• Strong Python, C++, and ROS 2 development skills, with experience building modular robotics software. 

• Familiarity with AI/ML workflows for robotic perception, learned manipulation, multimodal robotics, and autonomous systems. 

• Strong understanding of robotic manipulation, perception, motion planning, controls, coordinate frames and transformations, simulation, and automation software. 

• Experience with inverse kinematics, trajectory generation and execution, collision checking, robot state representation, gripper control, and integration of manipulators, sensors, and other automation hardware. 

• Experience developing or integrating perception systems for detection, segmentation, pose estimation, RGB-D, point clouds, or grasp generation, and integrating AI/perception models into real-time or near-real-time applications. 

• Experience debugging complex robotic systems across software, simulation, AI, networking, and physical hardware interfaces. 

• Strong analytical, troubleshooting, communication, and technical documentation skills, with the ability to independently manage complex assignments and collaborate across disciplines. 

Preferred qualifications 

• 5+ years of experience in robotics software, industrial robotics, AI-enabled robotics, manipulation, or automation systems. 

• Experience with FANUC, Universal Robots, collaborative or industrial manipulators, grippers, wrist-mounted cameras, depth sensors, industrial automation, material handling, robotic pick-and-place, machine tending, factory simulation, virtual commissioning, or plant-floor robotic applications. 

• Experience with MoveIt/MoveIt Task Constructor, Lula, RMPflow, OMPL, NVIDIA Isaac Sim/Isaac Lab, Omniverse Replicator, or similar frameworks. 

• Experience with VLA models, multimodal robotics, imitation learning, learned manipulation policies, synthetic data, domain randomization, automated annotation, simulation-to-real transfer, or simulated-environment training. 

• Experience with 3D perception, including RGB-D, point clouds, 6D pose estimation, registration, and geometric computer vision. 

• Experience deploying AI models with PyTorch, TensorRT, ONNX Runtime, or comparable GPU-accelerated inference frameworks. 

• Experience optimizing control-loop timing, perception and inference latency, trajectory execution, and end-to-end task cycle time. 

• Experience taking robotic systems from research or prototype environments through integration, validation, and deployment in production or manufacturing settings. 

 

On-site 5x days per week, Mountain View, CA

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area.

The salary range for this role is $153,200 to $234,100. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.

Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.

Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

This role is categorized as onsite. This means the selected candidate is expected to report to a specific location on a full-time basis.

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