{"id":743009,"url":"https://alion.io/job/gravisrobotics-machine-learning-engineer-sim2real-machine-modeling","title":"Senior Machine Learning Engineer - Sim2Real & Machine Modeling","company":{"id":673830,"name":"Gravis Robotics","domain":"gravisrobotics.com","url":"https://alion.io/company/gravisrobotics","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Zurich, Switzerland"],"countries":["CH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":109000,"max_usd":274000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1507},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Copilot","optional":false},{"name":"Machine Learning","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Imitation Learning","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-09-09T13:49:05Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-09-28T23:13:08Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":19},"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.\nGravis 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.\nBacked 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.\nThe 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.\nAbout the Role\nAutonomy 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.\nWhat you will do\nMachine & dynamics modeling\nBuild ML models to help bridge the sim2real gap\n\nDecide what architecture the problem actually needs - sequence models, state-space formulations, something else - and back the choice with data\n\nCharacterize where the gap actually comes from: which unmodeled effects hamper the sim2real transfer, and which we can safely ignore\n\nAnswer how much data is needed and what distribution it has to cover\n\nPerformance monitoring\nDefine the performance metrics and validation methodology for model fidelity and sim2real transfer\n\nBuild models and methods that detect machine properties changing over time\n\nWork closely with the autonomy and simulation teams - your models influence the controllers that run on the machine\n\nWhat we're looking for\nRequired\nDegree in Computer Science, Robotics, Machine Learning, Engineering, or a related field\n\nStrong Python and PyTorch, strong git skills\n\nSolid experience modeling time-series or dynamical-system data from large datasets - sequence models, system identification, or state-space approaches\n\nStrong analytical skills: you design the experiment, run the ablation, and draw a conclusion you'd defend\n\nNice to have\nReinforcement learning experience\n\nImitation learning or learning from demonstration, especially from human operator data\n\nFamiliarity with recent literature and methods in learned behavior policies\n\nClassical system identification, control, or hydraulics background\n\nThis role is a great fit if…\nYou like to solve problems outside of the laboratory\n\nYou 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\n\nYou 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\n\nYou'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","description_format":"text","description_chars":3419,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Switzerland","iso":"CH","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-11T13:12:02Z"}],"liveness":{"score":70,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.774,"p_room":0.9,"age_days":18,"expected_fill_days":42,"reasons":["conf:6","win:mid"],"computed_at":"2026-09-28T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/gravisrobotics-machine-learning-engineer-sim2real-machine-modeling","json_url":"https://alion.io/job/gravisrobotics-machine-learning-engineer-sim2real-machine-modeling.json","meta":{"generated_at":"2026-09-29T03:35:28Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3317,"day_limit":5000,"remaining_today":1683,"minute_limit":60,"resets_at":"2026-09-30T00:00:00Z"}}}