Machine Learning Engineer - Sim2Real & Machine Modeling in Zürich

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 RoleAutonomy 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 DoMachine & dynamics modelingBuild ML models to help bridge the sim2real gapDecide what architecture the problem actually needs - sequence models, state‑space formulations, something else - and back the choice with dataCharacterize where the gap actually comes from: which unmodeled effects hamper the sim2real transfer, and which we can safely ignoreAnswer how much data is needed and what distribution it has to coverPerformance monitoringDefine the performance metrics and validation methodology for model fidelity and sim2real transferBuild models and methods that detect machine properties changing over timeWork closely with the autonomy and simulation teams — your models influence the controllers that run on the machineWhat we're looking forRequiredDegree in Computer Science, Robotics, Machine Learning, Engineering, or a related fieldStrong Python and PyTorch, strong git skillsSolid experience modeling time‑series or dynamical‑system data from large datasets - sequence models, system identification, or state‑space approachesStrong analytical skills: you design the experiment, run the ablation, and draw a conclusion you'd defendNice to haveReinforcement learning experienceImitation learning or learning from demonstration, especially from human operator dataFamiliarity with recent literature and methods in learned behavior policiesClassical system identification, control, or hydraulics backgroundThis role is a great fit if…You like to solve problems outside of the laboratoryYou 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 numbersYou 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 itYou'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 stepsThis is an opportunity to join a dynamic, multidisciplinary team and to be part of a company that is reshaping heavy construction.Gravis 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.We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. #J-18808-Ljbffr