{"id":2874674,"url":"https://alion.io/job/fieldai-robotics-applied-scientist-spatio-temporal-scene-understanding-memory","title":"Robotics Applied Scientist, Spatio-Temporal Scene Understanding & Memory","company":{"id":672883,"name":"FieldAI","domain":"fieldai.com","url":"https://alion.io/company/fieldai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":82,"open_postings":106,"ghost_share":0.217,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":65,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Robotics","role_family":"Robotics","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":["Irvine, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":126000,"max_usd":305000,"period":"year","method":"role_country_seniority_unknown","sample_n":590},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Digital Twin","optional":false},{"name":"Python","optional":false},{"name":"ROS","optional":false},{"name":"SLAM","optional":false},{"name":"World Models","optional":false}],"status":"live","first_seen_at":"2026-10-19T05:07:25Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-28T21:41:26Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":8},"description":"What You Will Get To Do\nInvestigate the appropriate representation for the robot's mission memory: a persistent spatio-temporal representation of what it saw, where, when, and how it changed, across missions and sites, so assets that move, disappear, or degrade are tracked rather than rediscovered.\n\nBuild world models that let the robot reason and plan over that memory and recover physical parameters through interaction for physics-aware digital twins.\n\nCombine learned and physically grounded models, with geometry and dynamics as the ground truth.\n\nBridge the perception-reasoning gap for long-term autonomy.\n\nWhat You Have\nPhD in robotics, computer science, electrical engineering, aerospace engineering, mechanical engineering, or a related field, with publications at top venues such as RSS, CoRL, ICRA, IROS, T-FR, T-RO, CVPR, NeurIPS, or similar, on scene understanding, semantic SLAM, lifelong or spatio-temporal mapping, world models, or embodied reasoning.\n\nHands-on experience with vision-language or foundation models applied to spatial reasoning, memory, or long-horizon embodied question answering.\n\nDepth in 3D representations: scene graphs, neural or Gaussian-splat maps, or object-centric maps, and the estimation underneath them.\n\nStrong Python; 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