VR computer vision jobs power the perception layer of modern XR: tracking, mapping, and understanding the user’s environment in real time. These roles are common on headset teams and XR platform teams, where reliable inside‑out tracking and scene understanding are core product capabilities. If you like applied research that must ship at scale, XR perception roles are a practical way to work on AI and computer vision with tight performance constraints.
A typical position may involve visual-inertial odometry (VIO), SLAM, spatial mapping, hand tracking, controller tracking, object detection, or depth estimation. You might work on feature extraction and matching, sensor fusion, calibration, robustness in difficult environments, or ML models that run efficiently on-device. Many teams combine classical geometry with modern deep learning, and the most effective systems are engineered end-to-end: data collection, training, runtime optimization, and evaluation.
Tooling and languages often include C++ for production pipelines, Python for experimentation and training, and frameworks like PyTorch for models. Performance engineering is a constant theme: latency budgets are strict, power is limited, and inference must be stable across hardware variations. Experience with GPU programming, SIMD, mobile optimization, or embedded constraints can be a differentiator. You may also collaborate with hardware teams on sensor selection and with product teams on quality metrics.
Keywords to look for include SLAM, VIO, sensor fusion, tracking, hand tracking, spatial mapping, perception, and on-device ML. These are some of the most technically challenging—and impactful—roles in the VR ecosystem.
