Machine Learning Engineer - Sim2Real & Machine Modeling

ppGravis 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. /p pGravis 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. /p pBacked 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. /p pThe 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. /p br/ h3About the Role /h3 pAutonomy 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. /p h3What you will do /h3 h3Machine dynamics modeling /h3 ul lipBuild ML models to help bridge the sim2real gap /p /li lipDecide what architecture the problem actually needs - sequence models, state-space formulations, something else - and back the choice with data /p /li lipCharacterize where the gap actually comes from: which unmodeled effects hamper the sim2real transfer, and which we can safely ignore /p /li /ul ul lipAnswer how much data is needed and what distribution it has to cover /p /li /ul h3Performance monitoring /h3 ul lipDefine the performance metrics and validation methodology for model fidelity and sim2real transfer /p /li lipBuild models and methods that detect machine properties changing over time /p /li /ul ul lipWork closely with the autonomy and simulation teams - your models influence the controllers that run on the machine /p /li /ul h3What we're looking for /h3 pbRequired /b /p ul lipDegree in Computer Science, Robotics, Machine Learning, Engineering, or a related field /p /li lipStrong Python and PyTorch, strong git skills /p /li lipSolid experience modeling time-series or dynamical-system data from large datasets - sequence models, system identification, or state-space approaches /p /li lipStrong analytical skills: you design the experiment, run the ablation, and draw a conclusion you'd defend /p /li /ul pbNice to have /b /p ul lipReinforcement learning experience /p /li lipImitation learning or learning from demonstration, especially from human operator data /p /li lipFamiliarity with recent literature and methods in learned behavior policies /p /li lipClassical system identification, control, or hydraulics background /p /li /ul h3This role is a great fit if… /h3 ul lipYou like to solve problems outside of the laboratory /p /li lipYou 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 /p /li lipYou 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 /p /li lipYou'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 /p /li /ul br/ pThis is an opportunity to join a dynamic, multidisciplinary team and to be part of a company that is reshaping heavy construction. /p pGravis is an equal opportunity employer. We are committed to building an inclusive and diverse team, and do not discriminate based on race, colour, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics. /p /p #J-18808-Ljbffr