Cartesian is building spatial intelligence for indoor environments to drive operational efficiency by tackling the in-store inventory visibility challenge in the global retail industry. Their platform delivers accurate indoor positioning and actionable product location insights to help retailers streamline operations, optimize workflows, and reduce inefficiencies.
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).
- 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 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.
- Past startup experience
- Industry experience in applied software or ML engineering.
