{"id":3841931,"url":"https://alion.io/job/maxinsights-embodied-ai-real2sim-engineer","title":"Embodied AI Real2Sim Engineer","company":{"id":2224833,"name":"Maxinsights","domain":"maxinsights.ai","url":"https://alion.io/company/maxinsights","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Blender","optional":false},{"name":"Isaac Sim","optional":false},{"name":"MuJoCo","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NeRF","optional":false},{"name":"NVIDIA PhysX","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Time Series","optional":false},{"name":"C++","optional":true},{"name":"Embodied AI","optional":true},{"name":"Python","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-11-05T22:49:17Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-08T10:58:21Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Description:\nCore Objective: Build the digital pipeline from the real world to simulation space. Reconstruct and calibrate visual and physical properties of robots, objects and scenes, and deliver SimReady interactive assets to reduce the sim-to-real gap, supporting algorithm training, simulation evaluation and policy transfer to real hardware.\nResponsibilities\nOwn the end-to-end Real2Sim workflow based on Isaac Sim, MuJoCo, Genesis, Blender and other simulation platforms, including 3D reconstruction, appearance reconstruction, dynamic identification, calibration of contact, friction and inertia parameters, to align dynamic and perceptual behaviors between simulation and real robots.\n\nCreate, validate and iterate SimReady assets following USD / URDF / MJCF standards. Configure joints, collision shapes, mass/inertia, joint limits, semantic labels and affordance properties for robots and interactive objects, to achieve physical parameter calibration of simulation assets, ensuring stable usability in simulation training.\n\nProcess multi-modal real-world captured data: camera calibration, point cloud & mesh processing, texture and PBR material calibration. Leverage 3DGS / NeRF and related reconstruction methods to digitize real scenes and export assets compatible with simulation engines.\n\nDevelop automated tools for calibration, data processing and asset quality inspection. Document asset specifications, calibration workflows and evaluation metrics to improve pipeline throughput and sim-real alignment accuracy.\n\nCollaborate with perception, robot learning, simulation engine and hardware teams, design quantitative sim-real benchmark experiments, identify simulation bias, and continuously improve calibration protocols and asset standards for closed-loop data iteration.\n\nDesign excitation trajectories for robot system identification, collect time-series dynamic data from physical robots, calibrate and reproduce non-ideal dynamic behaviors including nonlinear joint friction and transmission backlash.\n\nRequirements\nMust-have\nProficiency in at least one simulation/graphics tool such as Isaac Sim, MuJoCo, Genesis or Blender, with solid understanding of computer graphics and rigid-body dynamics. Familiar with physics simulation engines such as PhysX and Newton.\n\nHands-on experience with Real2Sim pipelines and SimReady asset standards; capable of independently building, configuring and validating robot or object assets including joint setup and physical property tuning.\n\nKnowledge of 3D reconstruction, camera imaging, point cloud processing and camera calibration; fundamental understanding of system identification, parameter calibration and contact/friction models for physical simulation. Familiar with PBR rendering and material creation.\n\nAbility to decompose requirements, implement technical solutions, run comparative experiments and root-cause sim-real mismatches from visual reconstruction or physical simulation.\n\nCapable of designing dynamic system identification experiments, analyzing measured hardware data, and quantifying sim-real errors introduced by nonlinear dynamics.\n\nNice-to-have\nProven track record delivering Real2Sim projects, especially for robotic arms, dexterous hands or humanoid robots.\n\nDeep familiarity with USD / URDF / MJCF, experience building asset libraries and automated asset validation pipelines.\n\nSkilled in Python / C++ for calibration scripts, engine extensions and data processing tools.\n\nExperience with 3DGS, NeRF, PBR reconstruction and photogrammetry.\n\nBackground in system identification, excitation trajectory design, dynamic parameter estimation and contact model tuning.\n\nUnderstanding of Sim2Real, reinforcement learning and embodied AI closed-loop systems.\n\nExperience with motion capture, multi-sensor calibration and real robot hardware experiments.\n\nFamiliar with nonlinear friction models such as LuGre and Stribeck, with experience modeling and tuning joint hysteresis and backlash.\n\nDefault Benefits:\nHealth insurance\n\nVision care\n\nDental coverage\n\n401(k)\n\nPaid holidays\n\nPTO (Paid Time Off)\n\nSick leave","description_format":"text","description_chars":4100,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Health insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Robotics AI","AI Training Data & Annotation"],"lifecycle":[{"event":"open","at":"2026-11-05T23:13:58Z"}],"visa":[{"country":"US","licensed_sponsor":true,"adverts_offering_sponsorship_24m":0,"sanctioned_now":false,"evidence":"H-1B filings in 12 months: 6","filings_12m":6,"filings_prev_12m":1,"green_card_filings_12m":0,"median_offered_wage_usd":162500,"route":null,"cap_exempt":false,"checked_at":"2026-11-08T10:45:15+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":4}],"liveness":{"score":90,"band":"hot","label":"Hiring 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