Your New Role and Team
Sanctuary AI delivers production-ready Physical AI to industry: learned policies that run on commercially available robot arms and do real factory work today. Our edge is dexterity. We design and build a hydraulically actuated, high-degree-of-freedom hand with integrated tactile and proprioceptive sensing, and we train it to do the fine, contact-rich work that grippers cannot.
We are looking for a researcher to own how that hand learns. You will bring hand-level dexterity into vision-language-action and world-model policies, build the demonstration-data engine that feeds them, and make touch a first-class input to learned behavior. You will work alongside the hardware, controls, and teleoperation teams that build and run the hand, deploy on real customer tasks, and shape the next generation of the hand.
We hire globally for this role. We sponsor immigration, support relocation to Vancouver.
Why This Role?
A hand few researchers get to work on. Most robot-learning scientists spend their careers on parallel-jaw grippers or off-the-shelf research hands with no path to deployment. Here you train a human-like, tactile-sensed hand that our own team designs, builds, and iterates, and your results change what the next hand looks like.
The unsolved part of the stack. Foundation-model policies are only now reaching hand-level dexterity. Contact-rich, multi-fingered manipulation is where the field's hardest open problems and most visible results are, and it is the problem this role exists to solve.
Real work, not demos. Our policies run on industrial arms doing real tasks for real customers. You will see your research deployed, measured, and used.
Ownership. You set the dexterity learning agenda, with the hardware, controls, and teleoperation teams across the room and a modern GPU cluster behind you. You are the person who decides how the hand learns.
Publish. We encourage publication at top venues, support conference travel, and open-source where it makes sense.
A low-friction move. We hire from anywhere, sponsor work permits through Canada's fast-track programs for tech talent, support permanent residency, and cover relocation. Vancouver is a safe, English-speaking city on the Pacific, in the same time zone as the Bay Area, with public health care and strong schools.
Success Criteria
Dexterity in foundation-model policies. Bring high-degree-of-freedom hand control into vision-language-action and world-model policies: action representation, conditioning on touch and proprioception, pre-training and fine-tuning, and on-robot evaluation.
The dexterous data engine. Shape how demonstrations from human hands and teleoperation are collected, retargeted to the robot hand, filtered, and scaled into training data. Decide what data matters and how much of it we need.
Touch in the loop. Make tactile and proprioceptive sensing a first-class input to learned policies, from representation and fusion through deployment on hardware.
Research leadership. Set the dexterity learning agenda, build rigorous hardware evaluation, publish where it makes sense, mentor other researchers, and feed what you learn back into the design of future hands. Simulation and reinforcement learning are tools in the kit where they help.
Your Experience
Qualifications
You are an independent researcher. You have taken a manipulation problem from question to working result on real hardware, with the method, experiments, and evaluation designed by you. You can scope and prioritize your own work from a high-level goal, recognize when a direction is failing, and communicate results clearly to engineers who are not ML researchers.
You know the difference between a gripper and a hand. You have hands-on experience with multi-fingered, high-degree-of-freedom hands, real or simulated: in-hand manipulation, finger gaiting, tactile-conditioned policies, or dexterous teleoperation and retargeting. You understand what changes in action-space design, contact modeling, observation choices, and transfer when the end effector has twenty or more degrees of freedom.
You have the track record. A PhD in machine learning, robotics, or a related field, or equivalent experience, with first-author work in robot learning and policies you have deployed on physical systems.
You have depth in at least two of:
Imitation learning and demonstration-data pipelines for manipulation (teleoperation, human video, retargeting, data curation)
Training, fine-tuning, or adapting vision-language-action or world-model policies for robot control
Tactile and multimodal sensing in learned policies
Reinforcement learning for robotics at scale, including simulation and sim-to-real transfer
You are fluent in Python and PyTorch and comfortable working inside a real robot software stack (ROS 2 or similar).
Strong pluses
Publications at CoRL, RSS, ICRA, IROS, NeurIPS, or ICML on dexterous manipulation or robot learning
Experience shaping hardware or sensor requirements from the learning side
Experience with dexterous teleoperation systems or motion-capture gloves
Mentoring or leading small research teams
Traits
Above all else, a consistently positive attitude and a willingness to do whatever it takes to create robust solutions to complex problems
Strong leadership skills in organizing R&D work for ML projects
Eager to take on new challenges with tenacity and positivity
Patience, persistence, and attention to detail when resolving performance issues
Enthusiasm for bringing human-like intelligence to machines
Ability to drive development of new functionalities from concept to production
Ability to multitask and prioritize in a fast paced environment
