Staff Applied Scientist - Ai & Robotics

gmpdc.ca

Base: 198,000 - 260,000; bonus/equity: an incentiv...
Hybrid
Robot learning architectures
End-to-end policy training pipelines
Multimodal sensory data integration
The Staff Applied Scientist position at General Motors focuses on leading the development of advanced robot learning systems for dexterous manipulation and autonomy in real-world environments. The role requires expertise in AI architectures, machine learning models, and robotics, with a strong emphasis on collaboration and innovation

Job Summary

  • Our AI Research team is building end-to-end robot policies that enable dexterous manipulation in real-world environments.
  • You will design, prototype, and deploy robot learning models that span perception, policy learning, simulation, and real-world execution.
  • You will help build robotic agents that can manipulate the world with dexterity and autonomy.

Matching Summary

Match Score: 85

The Staff Applied Scientist position at General Motors focuses on leading the development of advanced robot learning systems for dexterous manipulation and autonomy in real-world environments. The role requires expertise in AI architectures, machine learning models, and robotics, with a strong emphasis on collaboration and innovation.

Salary

Base: 198,000 - 260,000; Bonus/Equity: 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.

Skills & Requirements

Must-have

  • Robot learning architectures
  • End-to-end policy training pipelines
  • Multimodal sensory data integration
  • Large-scale AI architectures
  • PyTorch implementation skills
  • ROS/ROS2 practical experience

Nice-to-have

  • Dexterous manipulation systems
  • Robotics perception expertise
  • Simulator to real-world transfer
  • Foundation model adaptation

Key Requirements

  • PhD in relevant STEM field or Master's with equivalent industry experience
  • Proven experience building and deploying ML models on robotic systems
  • Demonstrated impact via robot learning publications, open-source contributions, or production robotics deployments

Work Rights

Not specified

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