Job description

About Tutor We believe robots will be the next chapter in making physical labor safer and more affordable. But, nearly nine out of ten American factories and warehouses don’t have a single robot. Tutor Intelligence is changing that. We build robots that go straight into real factories and warehouses, to do real work, with real impact. Our robot Cassie does back-breaking work, moving boxes, pallets, and bulk goods for family-owned manufacturers to some of the largest brands in the world. Our robot Sonny does handwork, using two arms to achieve human-like dexterity in picking and packing. Every robot learns on the job, and each one we deploy makes the next one more capable, more affordable, and faster to put to work. Founded at MIT and headquartered in Watertown, MA, we do it all in-house: the robotics hardware, the machine learning, and the field deployment and service. We’re a team of researchers, engineers, technicians, operators, and sellers who love building things and solving hard problems. Our mission is grand: robots everywhere, for the common good, raising the standard of living across North America, and then the world. The Role Build perception that makes robot manipulation experiments reliable. You will own a significant vision system from problem definition and data collection through evaluation, integration, and ongoing improvement on physical robots. The work combines camera geometry and learned models, with decisions grounded in measured performance. You will work closely with research, software, mechanical, and electrical engineers to understand failures across sensing, algorithms, and the robot environment. This role focuses on perception and its integration into manipulation systems. Responsibilities Define the technical approach and success criteria for ambiguous perception problems in robot manipulation. Build and integrate capabilities such as detection, segmentation, tracking, object pose estimation, and 3D scene understanding. Own camera calibration, coordinate transforms, synchronization assumptions, and checks that expose degraded sensor quality. Create reproducible datasets and evaluations covering changes in objects, lighting, viewpoint, and occlusion; prevent evaluation leakage. Select and adapt geometric methods and learned models based on accuracy, latency, robustness, and operational constraints. Deploy and maintain perception in the robot stack; diagnose failures using recorded data and physical experiments. Improve shared evaluation tools and documentation, contribute to design reviews, and mentor colleagues in your domain. Requirements Evidence of independently owning a substantial computer-vision system from an ambiguous problem through tested integration. Strong Python engineering and experience writing maintainable code, tests, and reproducible experiments. Practical command of camera models, calibration, coordinate frames, and 3D geometry. Experience with geometric vision or learned visual models, with the judgment to explain when each approach is appropriate. Ability to build representative evaluations, isolate failure modes, and distinguish model improvements from data or measurement artifacts. Ability to communicate technical tradeoffs and resolve interfaces with engineers in other disciplines. Nice to have Perception deployed on physical robots, especially manipulation systems. Multi-camera or depth systems, sensor fusion, SLAM, or object pose estimation. OpenCV, Open3D or PCL; a modern deep-learning framework; C++ or GPU optimization. What Success Looks Like Establish a reproducible baseline, meaningful evaluation, and documented sensing assumptions for an agreed perception problem. Deliver a measured improvement that holds up on the physical robot and meets runtime constraints. Keep the system diagnosable and usable by colleagues through reliable calibration checks, failure analysis, and documentation. Why Tutor? Whether you’re designing robots, closing deals, running deployments, or creating the processes that help us scale, your work doesn’t sit on a shelf. It shows up on real production floors, making physical work safer and more affordable. Our team of 160+ is driven by curiosity and a genuine love of building. Everyone here takes real ownership, learns fast, and holds a high bar while having each other’s backs. If you love hard problems and creating things that didn’t exist before, you’ll fit right in. Benefits We take care of our team so they can do their best work. At Tutor, you’ll get: Fully employer-covered health insurance, so you’re protected without the premium. A managed 401(k) to help you plan for the future. Generous time off: unlimited PTO and all federal holidays off for full-time salaried employees, and 16+ days of accrued paid time off plus 1.5x pay on federal holidays for hourly employees. Regular in-office meals and team social events throughout the year. As Tutor grows, so will our benefits. We’re continuing to invest in ways to support our team at work and beyond. Tutor Intelligence is an equal opportunity employer and welcomes applicants from all backgrounds.

Requirements

Must-haves

Define the technical approach and success criteria for ambiguous perception problems in robot manipulation.
Build and integrate capabilities such as detection, segmentation, tracking, object pose estimation, and 3D scene understanding.
Own camera calibration, coordinate transforms, synchronization assumptions, and checks that expose degraded sensor quality.
Create reproducible datasets and evaluations covering changes in objects, lighting, viewpoint, and occlusion; prevent evaluation leakage.
Select and adapt geometric methods and learned models based on accuracy, latency, robustness, and operational constraints.
Deploy and maintain perception in the robot stack; diagnose failures using recorded data and physical experiments.
Improve shared evaluation tools and documentation, contribute to design reviews, and mentor colleagues in your domain.
Evidence of independently owning a substantial computer-vision system from an ambiguous problem through tested integration.
Strong Python engineering and experience writing maintainable code, tests, and reproducible experiments.
Practical command of camera models, calibration, coordinate frames, and 3D geometry.
Experience with geometric vision or learned visual models, with the judgment to explain when each approach is appropriate.
Ability to build representative evaluations, isolate failure modes, and distinguish model improvements from data or measurement artifacts.
Ability to communicate technical tradeoffs and resolve interfaces with engineers in other disciplines.

Nice-to-haves

Perception deployed on physical robots, especially manipulation systems.
Multi-camera or depth systems, sensor fusion, SLAM, or object pose estimation.
OpenCV, Open3D or PCL; a modern deep-learning framework; C++ or GPU optimization.
What Success Looks Like
Establish a reproducible baseline, meaningful evaluation, and documented sensing assumptions for an agreed perception problem.
Deliver a measured improvement that holds up on the physical robot and meets runtime constraints.
Keep the system diagnosable and usable by colleagues through reliable calibration checks, failure analysis, and documentation.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

gomachine learningpython

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

Vision

401(K)

Pto

Paid Time Off