Cartesian Systems
Postdoctoral Associate
Cambridge, MA · Full-time
Sponsorship not specifiedDetected 27 days ago
AlgorithmsMachine LearningDeep LearningMLOpsRoboticsElectrical EngineeringSignal ProcessingSensorsResearchCommunication
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.
Responsibilities
- 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
- Collaborate with engineering and product to ship features to enterprise customers
Requirements
- Excellent communication skills and ability to work in a small, fast-moving team.
- Duration: Minimum 1 year
- 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.
Nice to have
- Startup or early-stage company experience
- Work on hard, real-world problems with immediate and visible impact
- Join a small, highly technical team with significant ownership and autonomy
- In-person team culture in the heart of Kendall Square, Cambridge
- Introductory call to assess motivation and overall fit (20m)
- Technical interview focused on past projects (60m)
- Coding interview (45m)
- Fadel Adib (20m)
Skills
- About Cartesian
Apply directly at Cartesian Systems →Create a free account for alerts like thisView Cartesian Systems immigration profile
This listing is sourced directly from Cartesian Systems's careers page and normalized into a canonical job model.