Machine Learning Engineer

You will join a small, early team working at the frontier of ML for robotics and physical AI. Most of your time will go into understanding new papers, evaluating them, and turning the ones that work into product. You will see each piece through, from data and models to the backend, frontend and evals around them.

• Read recent papers in robot learning, simulation and vision. Implement them, find where they break, and propose improvements.
• Build data and evaluation pipelines: scene generation, simulated robot rollouts, graders and benchmarks. Most of our work is eval-backed: if we can't measure a change, it doesn't ship.
• Work across the product as needed: data models and APIs, React frontends, tests.
• Use agentic coding tools (Claude Code, Cursor, Codex etc.) as a normal part of building.
• Talk to customers, understand the real problem, and reason from first principles about what to build.

• Solid grasp of core ML and data science: model training, losses, generalisation, evaluation, statistics.
• Ability to read a recent paper, understand it in depth and implement it, then think about what to try next and how it could become a product.
• Experience running the end-to-end ML workflow, including dataset creation, third-party annotator coordination and review, and continuous evaluation.
• Experience identifying publicly available models and datasets, such as on Hugging Face, for quick deployments and early testing.
• Foundations in application development: data models / ORMs, React component models.
• Hands-on experience building with agentic development tools.
• Willingness to work on any part of the product: frontend, backend, testing, infra.
• Ability to write high-quality code, proven by prior work.
• Strong programming and CS skills, preferably through a BE/BTech undergraduate course, with significant ML projects and coursework.
• Comfort talking to customers, and the habit of thinking from first principles.
• High self-drive, self-structure, and the ability to work hard.

Python, PyTorch / Tensorflow and Kubeflow on Google Cloud; LeRobot, Isaac Sim / Isaac Lab and MuJoCo for robotics & simulation.

• Experience with simulators or robot learning stacks (Isaac Lab, MuJoCo, ManiSkill, LeRobot).
• Good clarity on image (preferably video) recognition networks, including standard object detection networks; 3D / scene reconstruction.
• Experience with end-to-end computer vision: data collection, training, optimization for mobile / on-prem deployment, and observability.
• Running GPU workloads on cloud VMs.
• Open-source contributions, publications, or a project others have used.

Point us to your favourite project: code, paper, demo or write-up. In the application form, tell us:

1. What problem it solved and why it mattered to you.
2. Your role. What exactly did you build?
3. The 2–3 key choices you made (model, data, architecture, eval) and the alternatives you rejected.
4. How you measured whether it worked, and what the results were.
5. What you would do differently now.

Also include: links to GitHub / portfolio, your CV, the agentic tools you use and one thing they changed about how you work, and a recent paper you found exciting and why.