Overview
On Site
$50 - $80
Accepts corp to corp applications
Contract - W2
Contract - Independent
Unable to Provide Sponsorship
Skills
Robot Learning Architectures
PyTorch
ROS/ROS2
Multimodal Perception
Diffusion Models
Transformers/VLM/VLAs
Policy Training Pipelines
Simulation (Isaac/Mujoco/etc.)
Closed-Loop Control
Real-World Robotics Deployment
Job Details
Role: Staff Applied Scientist AI & Robotics
Location: Onsite (5 days/week) Mountain View, CA
Interview Process: 1 technical video interview
Relocation: GM allows up to 2 weeks for candidates needing to relocate
About the Role:
You will be part of GM s cutting-edge AI Research team, building end-to-end robotic manipulation and embodied AI systems.
This work spans robot learning architectures, multimodal perception, simulation, and real-world deployment.
As a Staff Applied Scientist, you ll lead key components of robot policy development from model design and training pipelines to integration with perception, motion control, and hardware systems.
What You ll Do:
- Develop advanced robot learning architectures (diffusion policies, ACT, VLM/VLA-guided agents, imitation learning)
- Build end-to-end policy training pipelines using multimodal sensory data (RGB, depth, proprioception, tactile, LiDAR, force/torque)
- Implement policy inference and closed-loop control on real robotic platforms
- Integrate large-scale AI architectures (LLMs, VLMs/VLAs, diffusion models) for embodied tasks
- Deploy robot policies on hardware with strong focus on robustness and safety
- Lead data strategy for demonstrations, teleoperation, simulation, and evaluation
- Stay current with embodied AI research and mentor team members
Required Qualifications:
- PhD in a STEM field, or Master s with equivalent experience
- Experience building & deploying ML models on robotic systems
- Deep understanding of modern AI architectures (Transformers, diffusion models, VLM/VLAs, CNNs)
- Strong PyTorch experience (custom modules, optimization, debugging)
- ROS/ROS2 experience for manipulation and motion control integration
- Track record of robot learning research or production deployments
Preferred Skills:
- Experience in dexterous manipulation or autonomous multi-step task execution
- Expertise in robotics perception (3D understanding, tactile sensing, multimodal fusion)
- Simulator experience: Isaac Sim, Mujoco, Gazebo, PyBullet
- Experience adapting foundation models for embodied AI
- Proven impact in production robotics or reproducible research
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.