Meta AR/VR Job | Research Scientist, Machine Learning and Eye Tracking | Quest
Job（岗位）: Research Scientist, Machine Learning and Eye Tracking | Quest
Type（岗位类型）: Research
Citys（岗位城市）: Redmond, WA
Date（发布日期）: 2024-5-1
Summary（岗位介绍）
Reality Labs Research (RLR) brings together a world-class, cross-disciplinary R&D team to develop the next generation of AR and VR technologies. Come work alongside a world leading team of scientists and engineers to create the technology that makes AR and VR pervasive and universal. Our research focuses on developing comprehensive solutions to complex eye tracking problems, including both algorithms and hardware for AR and VR systems. We are currently seeking researchers with experience in machine learning for computer vision applications, to help build eye tracking models that leverage multiple sensing modalities and that are personalized for best performance on every user of the system.
Qualifications（岗位要求）
PhD degree in computer science, machine learning, or equivalent experience in a related field.
Experience building and shipping CV, ML, or optimization systems that are proven to be robust in real world use cases.
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
5+ years experience in machine learning for computer vision applications.
3+ years of programming experience in Python and experience with ML frameworks such as PyTorch.
Description（岗位职责）
Research and develop novel machine learning and computer vision based methods for eye tracking in AR/VR form factors.
Work with large-scale eye tracking data sets to build models that work across the diversity of the entire human population.
Combine data driven and 3D model based methods to deliver robust performance independent of device fit.
Incorporate data from multiple sensor modalities to build robust, efficient models.
Quantify the robustness and accuracy of these models using statistical analysis of the data.
Optimize models to deliver robust, always-on eye tracking in power-constrained platforms.
Categorize failures and mine the data for difficult edge cases, and use this to improve the worst-case performance of eye tracking algorithms.
Help to develop simulation and ground truth data collection tools that reduce the amount of data needed to deliver robust models.
Collaborate with other research scientists, hardware and software engineers to develop innovative machine learning techniques for eye tracking use-cases.
Additional Requirements（额外要求）
Experience with large-scale machine learning techniques like semi-supervised learning, weakly-supervised learning and online adaptation of ML models.
Experience building personalized models for Eye Tracking or other human tracking tasks such as body, face or hand tracking.
Experience delivering robust data-driven models incorporating multiple sensor modalities at different data rates.
Understanding of sensor and optics principles that impact computer vision applications, including MTF, quantum efficiency, refraction, reflectance, etc.
Experience with transformers for vision applications.
Proven track record in publishing papers in machine learning and/or computer vision conferences including CVPR, ICCV, ISMAR, ECCV, NeurIPS, AAAI, ICLR.
