{"id":743020,"url":"https://alion.io/job/gravisrobotics-senior-reinforcement-learning-engineer","title":"Senior Reinforcement Learning Engineer","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":272000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1271},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CARLA","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Isaac Lab","optional":false},{"name":"Isaac Sim","optional":false},{"name":"Motion Planning","optional":false},{"name":"MuJoCo","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"ROS","optional":false}],"status":"live","first_seen_at":"2026-06-24T15:45:27Z","employer_posted_date":"2026-06-24","last_verified_at":"2026-09-25T19:01:08Z","board_verified":true,"closed_at":null,"days_open":93,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":92},"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.\nAbout the Job\nThe autonomy team at Gravis builds autonomous systems for excavators operating in real construction environments. You will build control modules that run on many different machines, across many sites, with different soil conditions. We’re looking for a roboticist with data driven planning and/or control background, deep python expertise and good level of C++ proficiency.\nTo be successful in this role you should have experience working with real robots, tackling the challenges of sim2real transfer, and deploying robotic systems in a production environment.\nWhat you will do\nLearning-Based Planning and Control for Real Systems\nDevelop data driven planning and control systems for autonomous excavation that generalize across machine models and soil conditions\nContribute to simulation improvements that reduce or address the sim2real gap\nDefine data collection and curation pipelines for incorporating real data in policy training \nDesign experiments focused on continuous performance and robustness improvements.\nExplore the usage of adaptive and online reinforcement learning in deployed systems\nProvide mentorship and supervision for junior team members, interns, and students.\nSystem Integration\nIntegrate learned components into a larger software stack\nCollaborate with excavation and motion planning engineers\nBuild tools for analysing and evaluating the behavior of learned components\nWhat we’re looking for\nWe recognize that excellent candidates come from diverse backgrounds with various combinations of skills. If you meet most of the core qualifications below, we highly encourage you to apply.\nCore qualifications\n2-5 years industry experience developing Reinforcement learning systems for control and/or planning and deploying them on real robots with a customer. If you only have experience with simulation, you’re most likely not a good fit for this position.\n\nExperience with GPU accelerated simulation environments (e.g. IsaacSim/IsaacLab, CARLA, MuJoCo)\n\nStrong Python skills and experience with PyTorch or similar libraries\n\nProficiency in C++\n\nComfortable debugging real-world system behavior\n\nAbility and willingness to travel as required by business projects.\n\nGreat-to-Have Skills & Experience\nExperience with hydraulic machinery\n\nExperience with supervised learning or imitation learning\n\nResearch experience in reinforcement learning\n\nExperience deploying robotic systems at scale (e.g. hundreds of units)\n\nFamiliarity with ROS or similar robotics frameworks\n\nExperience with feature-flagged deployments, staged rollouts, or long-lived platforms\n\nExperience with data curation for ML applications\n\nExperience guiding, mentoring, or leading junior colleagues, students, or project teams.\n\nFamiliarity with or interest in utilizing AI coding tools.\n\nThis Role is a Great Fit If\nYou are passionate about building systems that work reliably in the real world\n\nYou want to help build a long-lived excavation planning and control system intended to scale and positively impact the entire construction industry.\n\nYou are comfortable working with the realities of imperfect data and noisy measurements.\n\nYou have a keen interest in bridging the sim2real gap and understanding the differences between simulation and physical environments.\n\nYou are excited to help drive technical direction in a growing team transitioning from prototyping to the product stage.\n\nYou value a collaborative team culture rooted in thoughtful design, creative thinking, mutual respect, and pragmatism.","description_format":"text","description_chars":4124,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"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":10,"band":"cold","label":"Long shot","p_open":1,"p_active":0.374,"p_room":0.28,"age_days":92,"expected_fill_days":42,"reasons":["conf:0","win:tail","crowd:"],"computed_at":"2026-09-25T05:45:01Z"},"pay":null,"html_url":"https://alion.io/job/gravisrobotics-senior-reinforcement-learning-engineer","json_url":"https://alion.io/job/gravisrobotics-senior-reinforcement-learning-engineer.json","meta":{"generated_at":"2026-09-26T04:10:16Z","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":4544,"day_limit":5000,"remaining_today":456,"minute_limit":60,"resets_at":"2026-09-27T00:00:00Z"}}}