Machine Learning Scientist

Cartesian Systems • Cambridge, MA

8d • Onsite

About The Position

Cartesian is building spatial intelligence for indoor environments to improve operational efficiency, with a focus on in-store inventory visibility. Its platform combines wireless signals and mobile computer vision to provide indoor positioning and product-location insights. Founded in 2023, the company reports that its product is deployed in over a dozen countries.

The Machine Learning Scientist will join the core R&D team and contribute to positioning algorithms and models, own new features, and help shape the technical roadmap. The role is based in person in Kendall Square, Cambridge, near MIT and the Charles River. The scientist will work with engineering and product teams to translate research into production and deliver features to enterprise customers.

Requirements

• PhD in computer science, electrical engineering, or a related field.
• Deep understanding of and hands-on experience with machine learning models for time-series data, including transformer-based architectures and probabilistic models such as state estimation.
• Publications in top-tier machine learning, vision, or systems venues, such as ACL, NeurIPS, CVPR, ECCV, ICCV, MobiCom, MobiSys, MLSys, ICASSP, ICML, ICRL, ICRA, or IROS.
• Ability to write high-quality production code.
• Excellent communication skills and ability to collaborate across disciplines.
• Ability to thrive in fast-paced, dynamic environments and take pride in producing high-quality work.

Nice To Haves

• Past startup experience.
• Industry experience in applied software or machine learning engineering.
• Familiarity with cloud-based model training and inference.
• Experience optimizing and deploying machine learning models in mobile environments.
• Background in wireless localization or radar signal processing.
• Experience with computer vision or multi-sensor fusion techniques, such as 2D/3D perception, pose estimation, tracking, or SLAM.

Responsibilities

• Design, develop, and deploy machine learning models for indoor positioning and perception.
• Optimize model architectures for performance and efficiency.
• Develop tools and datasets to benchmark performance in real-world conditions and at scale.
• Translate research into production pipelines.
• Collaborate with engineering and product teams to ship features to enterprise customers.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree