{"id":3558054,"url":"https://alion.io/job/cartesian-systems-machine-learning-scientist","title":"Machine Learning Scientist","company":{"id":4391902,"name":"Cartesian Systems","domain":"cartesian.systems","url":"https://alion.io/company/cartesian-3","size_band":"1-10","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cambridge, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":143000,"max_usd":317000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Machine Learning","optional":false},{"name":"Time Series","optional":false},{"name":"Localization","optional":true},{"name":"Radar Signal Processing","optional":true},{"name":"Sensor Fusion","optional":true},{"name":"SLAM","optional":true}],"status":"live","first_seen_at":"2026-10-15T18:59:26Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-11-01T15:43:22Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"About the Company\nCartesian is building spatial intelligence for indoor environments to drive operational efficiency.\nWe’re tackling one of the biggest challenges in the $35T global retail industry: in-store inventory visibility. Our platform delivers accurate indoor positioning and actionable product location insights, helping retailers streamline operations, optimize workflows, and reduce inefficiencies. By fusing wireless signals and mobile computer vision, we provide a uniquely scalable and infrastructure-free solution already deployed by international fashion brands.\nFounded by an MIT engineering professor and alum behind the award-winning, patented core technologies, Cartesian spun out in 2023. Originally backed by the prestigious SBIR Award from the US National Science Foundation, we've bootstrapped to a live product that's now deployed in over a dozen countries and have been aggressively scaling in the market.\nAbout the Role\nWe’re looking for a highly motivated, product-oriented Machine Learning Scientist to join our core R&D team at a pivotal moment in our growth. You'll have a direct impact on key positioning algorithms & models, take ownership of new features, and help shape the technical roadmap of a category-defining product. We move fast, care deeply about quality, and value people who take initiative and crave real-world impact. \nYou’ll be joining us in-person in the heart of Kendall Square, Cambridge, next to MIT and the Charles River.\nResponsibilities\nDesign, develop, and deploy ML models for indoor positioning and perception.\n\nOptimize model architectures for performance and efficiency.\n\nDevelop tools and datasets to benchmark performance in the real world and at scale.\n\nTranslate research into production pipelines.\n\nCollaborate with engineering and product to ship features to enterprise customers.\n\nQualifications\nPhD in computer science, electrical engineering, or related field.\n\nDeep understanding and hands-on experience in machine learning models for time-series data, including both transformer-based architectures and probabilistic models (e.g., state estimation). \n\nPublications in top-tier ML, vision, or systems venues (e.g., ACL, NeurIPS, CVPR, ECCV, ICCV, MobiCom, MobiSys, MLSys, ICASSP, ICML, ICRL, ICRA, IROS).\n\nAbility to write high-quality production code. \n\nExcellent communication skills and ability to collaborate across disciplines. \n\nThrive in fast-paced, dynamic environments and take pride in producing high-quality work. \n\nNice to have\nPast startup experience\n\nIndustry experience in applied software or ML engineering.\n\nFamiliarity with cloud-based model training and inference.\n\nExperience in optimizing and deploying ML models in mobile environments.\n\nBackground in wireless localization or radar signal processing\n\nExperience with computer vision or multi-sensor fusion techniques (e.g., 2D/3D perception, pose estimation, tracking, 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