Solution

As the demands on our built environment rise, labour shortages loom. With the world’s workforce increasingly moving away from undesirable tasks, the manufacturing, construction, and logistics industries critical to our daily lives are left exposed. By deploying our general-purpose humanoid robots in environments deemed hazardous or monotonous, we envision a future where human well-being is safeguarded while closing the gaps in critical global labour needs.

About The Role In this role, you will work on teaching our robots to manipulate the world around them. This is a role at the intersection of applied deep learning and robotics, and to be set up for success you need both experience of working with real robot hardware (e.g. identifying issues in control or teleop), and applied deep learning (you don’t have to be an expert on cutting edge neural network techniques, but you should be perfectly capable of curating data, fine-tuning a policy on that data and hypothesising potential mitigations when something doesn’t work).

What You’ll Do

• Post-train manipulation policies via behaviour cloning and RL; own the full loop from data to deployment.
• Come up with data preprocessing strategies to improve the quality of collected data.
• Work with the simulation team to set up RL training using digital twin, and then iterate on reward and simulation quality to ensure successful transfer to the real world.
• Partner with the data collection organization to drive data collection activities for a specific capability: specify what good data looks like, ensure diversity and coverage, and iterate on instructions.
• Expand observation and action spaces with new components required to support novel capabilities, and work with the Teleoperations team to expose these components to robot operators.
• Partner with Teleoperations and Controls teams to improve motion smoothness and teleoperation experience.
• Interface with hardware design team to ensure that manipulation team findings regarding the current generation of hardware are reflected in future designs.

We’re Looking For

• 3+ years working on robots (industry or research) with shipped artifacts to show for it. A good understanding of modern teleoperation and low-level control stack.
• Experience with neural network post-training.
• Familiarity with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training, PyTorch or JAX. Ability to profile & debug numerics and write maintainable research code.
• Good familiarity with modern software engineering practices.
• Ability to document experiments clearly and communicate trade‑offs crisply.

Nice to have

• Experience training VLA models for manipulation (autoregressive, diffusion or flow-matching based). Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks.
• Experience applying RL to robotics problems.
• Publications at top-tier robotics or deep learning conferences or equivalent open‑source contributions.

What We Offer

• Competitive salary plus participation in our Stock Option Plan
• Paid vacation with adjustments based on your location to comply with local labor laws
• Travel opportunities to our Vancouver and Boston offices
• Office perks: free breakfasts, lunches, snacks, and regular team events
• Freedom to influence the product and own key initiatives
• Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics
• Startup culture prioritising speed, transparency, and minimal bureaucracy

How to Apply

Does this role sound like the perfect fit for you?

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