Machine Learning Scientist
Confirmed on the employer's own hiring board on Nov 1, 2026. First seen by Alion on Oct 15, 2026.
About the Company
Cartesian is building spatial intelligence for indoor environments to drive operational efficiency.
We’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.
Founded 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.
About the Role
We’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.
You’ll be joining us in-person in the heart of Kendall Square, Cambridge, next to MIT and the Charles River.
Responsibilities
Design, develop, and deploy ML models for indoor positioning and perception.
Optimize model architectures for performance and efficiency.
Develop tools and datasets to benchmark performance in the real world and at scale.
Translate research into production pipelines.
Collaborate with engineering and product to ship features to enterprise customers.
Qualifications
PhD in computer science, electrical engineering, or related field.
Deep 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).
Publications in top-tier ML, vision, or systems venues (e.g., ACL, NeurIPS, CVPR, ECCV, ICCV, MobiCom, MobiSys, MLSys, ICASSP, ICML, ICRL, ICRA, IROS).
Ability to write high-quality production code.
Excellent communication skills and ability to collaborate across disciplines.
Thrive in fast-paced, dynamic environments and take pride in producing high-quality work.
Nice to have
Past startup experience
Industry experience in applied software or ML engineering.
Familiarity with cloud-based model training and inference.
Experience in optimizing and deploying ML models in mobile environments.
Background in wireless localization or radar signal processing
Experience with computer vision or multi-sensor fusion techniques (e.g., 2D/3D perception, pose estimation, tracking, SLAM).