Jobs 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.

Develop machine learning and dynamical-system models to close the simulation-to-reality gap for autonomous construction machines. Responsibilities include selecting and validating model architectures, identifying unmodeled machine effects, determining data requirements, monitoring model fidelity, detecting changing machine properties, and collaborating with autonomy and simulation teams. The role requires strong Python, PyTorch, Git, time-series modeling, experimentation, and analytical skills, with reinforcement learning, imitation learning, control, or hydraulics experience preferred.

Own the embedded Linux platform for autonomous construction machinery, including bootloader, kernel, device tree, root filesystem, hardware bring-up, drivers, OTA updates, CI/CD, recovery, and security hardening. Collaborate with hardware, firmware, autonomy, and perception teams to diagnose field failures and deliver reliable platforms for global machine fleets.

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.