Staff ML Research Scientist

5 days ago

Vancouver, British Columbia, Canada Harnham Full time

Staff Research Scientist – Robotics & Machine Learning

Location:
Vancouver (Hybrid – at least 2 days onsite)

Compensation:
$200–300k base + equity

A pioneering robotics and AI team is hiring a Staff Research Scientist to push the boundaries of embodied intelligence. In this role, you'll work end to end across research, simulation, and real-world deployment, developing ML-driven control systems for dexterous humanoid robots.

You'll shape the direction of reinforcement and imitation learning research, design scalable training and data pipelines, run large-scale simulation experiments, and bring cutting-edge algorithms onto robots equipped with advanced sensing capabilities. This is a rare opportunity to contribute research that transitions directly into physical, real-world performance.

What You'll Do

  • Develop and advance RL/IL algorithms for complex manipulation tasks
  • Lead high-impact research directions and guide experimentation strategy
  • Design training and data-collection systems that speed up deployment
  • Improve efficiency, reliability, scalability, and compute performance of learning systems
  • Run experiments in simulation and on real robots, debugging end to end
  • Diagnose failures and design robust, production-ready solutions
  • Collaborate closely with ML researchers, robotics engineers, and applied teams

What We're Looking For

  • PhD in ML, Robotics, Computer Science, Applied Math, or equivalent expertise
  • 5+ years building and deploying robotic manipulation systems (simulation + real robots)
  • 5+ years hands-on experience with RL/IL for real-world robotics
  • 4+ years developing large-scale parallel simulation pipelines
  • Strong background in continual learning and sim-to-real transfer
  • Experience transitioning ML research into production robotic systems
  • Publication record in top-tier robotics/AI venues (ICRA, IROS, CoRL)

Technical Strengths

  • Expert Python
  • Proficiency with PyTorch or TensorFlow
  • Experience with ROS2 and parallel compute (CUDA, OpenMP)
  • Deep understanding of RL theory and practical implementation

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