Job Description

Required Skills & Experience

• Hands-on experience training reinforcement learning (RL) or deep RL (DRL) policies for robotic manipulation tasks

• Proficiency with NVIDIA Isaac Sim (or Isaac Lab) for simulation and policy training

• Experience with robot teleoperation systems — hardware setup, data collection pipelines, and demonstration quality assessment

• Strong Python skills; familiarity with ROS 2

• Background in Sim-to-Real transfer for manipulation or locomotion tasks

Nice to Have Skills & Experience

• Experience with imitation learning or learning from human demonstration (e.g., ACT, Diffusion Policy)

• 3D asset creation pipeline: CAD → USD / URDF → sim-ready assets (Blender, Omniverse tools)

• Familiarity with 3D vision and scene reconstruction (depth cameras, point clouds, NeRF / Gaussian Splatting)

• Experience with foundation or grasp models (e.g., AnyGrasp, DexGraspNet, or VLA models)

• Edge compute and model optimization experience (quantization, deployment on GPUs)

• Embedded robotics experience (real-time control, force/torque sensing)

Job Description

Insight Global is looking for a Robotics Engineer in the Warren, Michigan area. The Engineer will be working in the full stack — from hardware integration and teleoperation-based data collection to simulation-based RL policy training and Sim-to-Real transfer. You’ll be hands-on with real robots and real factory parts, with a clear roadmap toward deployment at manufacturing sites. Other Responsibilities: • Teleoperation Framework: Develop and operate a dual-arm teleoperation system to collect high-quality human demonstration data for complex manipulation tasks (e.g., part extraction from tight dunnage, dunnage manipulation, regrasp operations). • RL Policy Training: Design, train, and evaluate reinforcement learning policies in NVIDIA Isaac Sim, targeting robust dual-arm behaviors including coordinated pick-and-place, dunnage handling, and bimanual alignment. • Sim-to-Real Transfer: Bridge the gap between simulation and the physical robot setup by fine-tuning trained policies and validating performance on real hardware. • Simulation Asset Development: Build and maintain physics-accurate simulation assets — from CAD to USD — to support training and testing of manipulation policies. • Perception Integration: Work with camera systems (Intel RealSense, ZED) and 3D vision pipelines to enable part detection and scene understanding. • System Integration: Integrate the learning stack with ROS 2 / MoveIt 2 for motion planning and execution on the physical dual-arm platform.

Compensation : $40/hr to $45/hr Exact compensation may vary based on several factors, including skills, experience, and education. Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.

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