<h2><strong>About Cartesian</strong></h2> <p>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. Leveraging wireless signals, we provide a uniquely scalable, infrastructure-free solution already deployed by international fashion brands.</p> <p>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.</p> <h2><strong>About the Role</strong></h2> <p>We are looking for a <strong>Postdoctoral Associate</strong> to join Cartesian at an exciting moment in our growth. This opportunity is a chance to contribute to core algorithms, dig deep into a deployed product, and ship changes that reach enterprise customers, while also driving new research directions in spatial AI. This is a hands-on role at the intersection of machine learning, perception, estimation, and signal processing, with direct impact on a deployed enterprise product. You’ll gain exposure to the full journey of research in production, working in a fast-paced, hands-on environment where your work makes a tangible impact.</p> <p>This role is product- and impact-driven. Due to the IP-sensitive nature of the core models and algorithms, this role is not expected to involve publications. </p> <p><strong>Location:</strong> In-person at the Cartesian HQ in Kendall Square, Cambridge.</p> <p><strong>Role type:</strong> Full-time, PhD postdoctoral position</p> <p><strong>Duration:</strong> Minimum 1 year</p> <h2><strong>What You’ll Do</strong></h2> <ul> <li>Explore new research directions in sensor fusion and spatial AI aligned with product needs</li> <li>Develop and improve algorithms for indoor positioning and spatial perception</li> <li>Run experiments on real-world customer deployments: collect data, analyze failures, propose fixes, and validate improvements</li> <li>Contribute to production ML and signal processing pipelines (modeling, evaluation, deployment)</li> <li>Collaborate with engineering and product to ship features to enterprise customers</li> </ul> <h2><strong>Qualifications</strong></h2> <ul> <li>About to complete or recently completed a PhD program in Computer Science, Electrical Engineering, Robotics, or a related field.</li> <li>Research track record of published work in top-tier CS/ML, vision, robotics, or related venues.</li> <li>Strong foundations in at least one of: machine learning, perception, sensor fusion, and/or signal processing. </li> <li>Demonstrated research output (e.g., strong publications, open-source projects, or impactful applied work)</li> <li>Excellent communication skills and ability to work in a small, fast-moving team.</li> <li>Curiosity, ownership, and a bias toward action and real-world impact</li> </ul> <h2><strong>Nice to Have</strong></h2> <ul> <li>Startup or early-stage company experience</li> <li>Experience shipping products (e.g., part of internships)</li> <li>Experience or strong interest in spatial AI (3D vision, SLAM, mapping, sensor fusion, geometric deep learning, …) is highly desirable.</li> </ul> <h2><strong>Why Cartesian</strong></h2> <ul> <li>Work on hard, real-world problems with immediate and visible impact</li> <li>Join a small, highly technical team with significant ownership and autonomy</li> <li>Build systems that are deployed and used globally</li> <li>Collaborative, thoughtful, low-ego culture focused on learning and execution</li> <li>In-person team culture in the heart of Kendall Square, Cambridge</li> </ul> <h2><strong>Interview Process</strong></h2> <ul> <li>Introductory call to assess motivation and overall fit (20m)</li> <li>Technical interview focused on past projects (60m)</li> <li>Technical design interview (45m)</li> <li>Coding interview (45m)</li> <li>Meet Prof. Fadel Adib (20m)</li> <li>Meet the team and references <br><br></li> </ul> <h2> </h2>