Sobre este puesto de Computer Vision Engineer en Tutorintelligence
Tutor Intelligence builds AI robotics systems and deploys them into the facilities that need them. We believe research and deployment should improve each other: real-world operation reveals the problems worth solving, and better technology makes robots more useful. Join a team where your designs, experiments, and code have a direct impact on physical systems.
Our Culture
We value technical excellence, collaboration, and respect. Engineers take ownership, make tradeoffs explicit, and help colleagues do better work. We move quickly through building, testing, and learning from what happens on the robot.
About 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.
- 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.
What Success Looks Like
About our Roles & Titles
At Tutor, we believe great engineers and researchers are defined by what they build and the impact they have — not where they sit in an org chart or what title they have. Therefore, everyone in our R&D org holds the title Member of Technical Staff (MoTS). Our job postings use standard titles so you can find us, but if you join Tutor, you'll be a MoTS — with a level that is determined through the interview process.
That also means we hire people, not slots. Work at Tutor evolves every quarter, and we set the expectation of flexibility from day one — it's common for people to start on one thing and shift to another based on where the team needs them most. A high technical bar across the board is what makes that flexibility possible: it's what allows people to contribute meaningfully whatever problem they take on.
