Cartesian is looking to solve the problem of in-store inventory visibility in the $35T global retail industry by building spatial intelligence for indoor environments to drive operational efficiency.
Requirements
- 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).
- Ability to write high-quality production code.
- 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)
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.
Other
- PhD in computer science, electrical engineering, or related field.
- Excellent communication skills and ability to collaborate across disciplines.
- Thrive in fast-paced, dynamic environments and take pride in producing high-quality work.
- 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 work in-person in the heart of Kendall Square, Cambridge, next to MIT and the Charles River.
