Machine Learning Engineer - Sim2Real & Machine Modeling

Job description

Gravis Robotics is a high-growth Series A start-up backed by SoftBank, bringing Physical AI to the construction industry, turning heavy construction machines into autonomous robots. Gravis began as an ETH Zurich spin-out, and our unique combination of learning-based automation and augmented remote control lets one operator safely conduct a fleet of earthmoving machines in a gamified environment. Backed by deep robotics research and now deployed across multiple countries with leading construction and equipment partners, our team is rapidly growing to bring this technology to a trillion-dollar industry. The Gravis RACK is a machine-agnostic retrofit kit that adds autonomy to excavators and wheel loaders from 10 to 100+ tonnes: LiDAR and camera sensing, GNSS RTK, networking hardware and rugged edge compute that works offline. Paired with the Slate tablet and our Copilot software, it lets an operator run a machine manually, with AI assistance, or fully autonomously. Increasingly, we also build custom hardware to adapt our machines for highly specialized, robust applications beyond traditional excavation. ### About the Role Autonomy team at Gravis heavily relies on simulation to develop autonomous controllers. Whether these controllers work on the machine depends on how well we close the sim2real gap. In this role you will help us bridge the gap. We are looking for someone with strong ML/RL background and experience with real robotic systems. ### What you will do **Machine & dynamics modeling** - Build ML models to help bridge the sim2real gap - Decide what architecture the problem actually needs - sequence models, state-space formulations, something else - and back the choice with data - Characterize where the gap actually comes from: which unmodeled effects hamper the sim2real transfer, and which we can safely ignore - Answer how much data is needed and what distribution it has to cover **Performance monitoring** - Define the performance metrics and validation methodology for model fidelity and sim2real transfer - Build models and methods that detect machine properties changing over time - Work closely with the autonomy and simulation teams — your models influence the controllers that run on the machine ### What we're looking for **Required** - Degree in Computer Science, Robotics, Machine Learning, Engineering, or a related field - Strong Python and PyTorch, strong git skills - Solid experience modeling time-series or dynamical-system data from large datasets - sequence models, system identification, or state-space approaches - Strong analytical skills: you design the experiment, run the ablation, and draw a conclusion you'd defend **Nice to have** - Reinforcement learning experience - Imitation learning or learning from demonstration, especially from human operator data - Familiarity with recent literature and methods in learned behavior policies - Classical system identification, control, or hydraulics background ### This role is a great fit if… - You like to solve problems outside of the laboratory - You like a culture where the best idea wins no matter whether it comes from the CTO or an intern, as long as it's backed by numbers - You are comfortable owning the result end to end: when the data you need doesn't exist yet, you go on site, touch the machine and get it - You'd take a simple model that measurably closes the gap over a sophisticated one that might, and you're patient enough to get there in steps