YUGENOX·JOBS

Postdoctoral Associate

Cartesian Systems · Cambridge, MA

R&D

About the role

About Cartesian

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.

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 are looking for a Postdoctoral Associate 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.

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.

Location: In-person at the Cartesian HQ in Kendall Square, Cambridge.

Role type: Full-time, PhD postdoctoral position

Duration: Minimum 1 year

What You’ll Do • Explore new research directions in sensor fusion and spatial AI aligned with product needs • Develop and improve algorithms for indoor positioning and spatial perception • Run experiments on real-world customer deployments: collect data, analyze failures, propose fixes, and validate improvements • Contribute to production ML and signal processing pipelines (modeling, evaluation, deployment) • Collaborate with engineering and product to ship features to enterprise customers

Qualifications • About to complete or recently completed a PhD program in Computer Science, Electrical Engineering, Robotics, or a related field. • Research track record of published work in top-tier CS/ML, vision, robotics, or related venues. • Strong foundations in at least one of: machine learning, perception, sensor fusion, and/or signal processing. • Demonstrated research output (e.g., strong publications, open-source projects, or impactful applied work) • Excellent communication skills and ability to work in a small, fast-moving team. • Curiosity, ownership, and a bias toward action and real-world impact

Nice to Have • Startup or early-stage company experience • Experience shipping products (e.g., part of internships) • Experience or strong interest in spatial AI (3D vision, SLAM, mapping, sensor fusion, geometric deep learning, …) is highly desirable.

Why Cartesian • Work on hard, real-world problems with immediate and visible impact • Join a small, highly technical team with significant ownership and autonomy • Build systems that are deployed and used globally • Collaborative, thoughtful, low-ego culture focused on learning and execution • In-person team culture in the heart of Kendall Square, Cambridge

Interview Process • Introductory call to assess motivation and overall fit (20m) • Technical interview focused on past projects (60m) • Technical design interview (45m) • Coding interview (45m) • Meet Prof. Fadel Adib (20m) • Meet the team and references

Text as published by Cartesian Systems. We don't edit postings; apply and ask questions on the employer's site.

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First seen by us
Nov 7, 2026
Still listed
Nov 11, 2026
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