Teams at Cartesian

Design, develop, optimize, and deploy machine learning models for indoor positioning and perception. Build benchmarking tools and real-world datasets, translate research into production pipelines, and collaborate with engineering and product teams to ship enterprise features. The role requires advanced expertise in time-series machine learning, transformer architectures, probabilistic models, and production software development, with opportunities to apply computer vision, sensor fusion, localization, and mobile ML optimization.

PhD intern will research and develop machine learning, perception, sensor fusion, and signal-processing algorithms for indoor positioning and spatial AI. Responsibilities include experimenting with real-world deployment data, analyzing failures, improving production ML pipelines, validating models, and collaborating with engineering and product teams to ship enterprise features. The role is hands-on, product-focused, and involves research in production rather than public publication.

Conduct research in sensor fusion and spatial AI; develop indoor positioning and spatial perception algorithms; run experiments on customer deployments; analyze failures; improve production machine learning and signal processing pipelines; and collaborate with engineering and product teams to ship enterprise features. The role is an in-person, full-time postdoctoral position requiring a PhD or imminent PhD completion.