Member of Technical Staff - Robotics

Veeda AI; Toronto; On-site; CAD 110,000 - 150,000; Full time

Job summary

Veeda AI is seeking a Member of Technical Staff – Robotics in Toronto to advance multimodal world models for Physical AI. You will train robot policies in simulation, evaluate sim2real transfer, and build rigs for data collection, collaborating with engineers and customers.

The role requires strong Python and PyTorch skills, ROS 2, and hands-on hardware experience. Travel on-site may be required to calibrate robots and run experiments.

Qualifications

• Bachelor's degree or equivalent hands-on experience in robotics, computer science, mechanical or electrical engineering.
• Experience training robot policies in simulation and deploying them on real hardware.
• Strong Python and PyTorch skills; fluency in a robot software stack (ROS 2 and ros2_control) including calibration.

Responsibilities

• Simulation product owner: use our simulation tooling for (post)training policies and provide feedback to the team.
• Sim-to-Real transfer: design experiments to measure the sim2real gap of the simulation stack.
• Teleoperation & Data Collection: build and run rigs that produce demonstration data.
• Interface with other roboticists: learn our ML language and communicate with customers and collaborators.

Skills

Python

PyTorch

ROS 2

Kinematics

Camera calibration

Education

Bachelor's degree in Robotics/CS/Engineering

Tools

ROS 2

ros2_control

Job description

Member of Technical Staff – Robotics

About Us

Veeda AI is building the next generation of multimodal foundation world models for Physical AI. We're a small, fast-moving team of engineers and researchers from leading AI labs, tackling some of the most challenging problems at the intersection of AI, robotics, and embodied intelligence. If you're excited about pushing the boundaries of what's possible with Physical AI, you'll have the opportunity to make an outsized impact from day one.

Responsibilities

• Simulation product owner: use our simulation tooling for (post)training policies (pi-0 family, GR00T N) and provide feedback to the rest of the team.

• Sim-to-Real Transfer: design experiments to measure the sim2real gap of our simulation stack, evaluate both simulation fidelity and policy improvements.

• Teleoperation & Data Collection: Build and run the rigs that produce demonstration data.

• Interface with other roboticists: learn our ML language and help us talk with customers, data providers and other collaborators who speak robot.

Requirements

• Bachelor’s degree or equivalent hands-on experience in Robotics, Computer Science, Mechanical or Electrical Engineering, or a related technical field

• Experience training robot policies in simulation and deploying them on real hardware

• Strong Python and PyTorch skills; fluency in a robot software stack (ROS 2 and ros2_control, or equivalent), including kinematics and camera-to-robot calibration

• Ability to design real-world experiments with randomized initial conditions and statistically meaningful results

• Willingness and ability to travel on-site bring up and calibrate robots

Nice to Have

• Experience with humanoid or quadruped whole-body control, or dexterous, contact-rich manipulation with force or tactile feedback

• Experience establishing a robot cell or data-collection operation from scratch

• Experience post-training vision-language-action models, including RL fine-tuning of flow-based policies

• Experience running large-scale parallel RL in Isaac Lab, MuJoCo Warp, Newton, or Genesis

• Publication or contribution to research on robot learning, imitation learning, or embodied AI

• Contribution to open-source robot learning projects such as LeRobot, robosuite, or ManiSkill