Teams at Gravis Robotics

Lead the MLOps strategy, roadmap, and engineering team supporting Gravis Robotics’ full machine-learning lifecycle. Responsibilities include building petabyte-scale multimodal data pipelines, operating hybrid cloud and on-premise Kubernetes infrastructure, deploying and monitoring models across thousands of edge devices, and establishing model registries, artifact and data versioning, experiment tracking, CI/CD, testing, and observability. The role also involves technical leadership, mentoring, tooling evaluation, and close collaboration with robotics engineers.

Open general application for candidates interested in joining Gravis Robotics. Submit your background and skills for review; the team will contact you if there is a match. Company builds autonomous earthmoving robots, requires English proficiency, is an equal-opportunity employer, and offers a Zurich-based workplace and fair market salary.

Develop data-driven planning and control systems for autonomous excavators, reduce sim2real gaps, integrate learned components into production stacks, define data pipelines, run experiments, and mentor junior team members while collaborating across engineering teams.