Research Scientist, Machine Learning and Eye Tracking Responsibilities
  • Research and develop novel Machine Learning and Computer Vision based methods in the area of personalized models for eye tracking.
  • Incorporate data from multiple sensor modalities to build robust, efficient models.
  • Work with large-scale user-study data collected from AR and VR eye trackers to build models that work across the diversity of the entire human population.
  • Explore multiple paths to personalized models, including using neural encodings to improve robustness
    working at the intersection of ML, Optimization, and Sensor systems to model how eye tracking sensors can best track the eye and leveraging 3D scans and other anatomical models of eyes as priors for better tracking.
  • Quantify the robustness and accuracy of these models using statistical analysis of the data.
  • Categorize failures and mine the data for difficult edge cases, and use this to improve the worst-case performance of eye tracking algorithms.
  • Collaborate with other research scientists, hardware and software engineers to develop innovative machine learning techniques for eye tracking use-cases

Minimum Qualifications

  • Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
  • PhD degree in Computer Science, Machine Learning, or equivalent experience in a related field.
  • 5+ years experience in machine learning for computer vision applications.
  • Experience building CV, ML, or optimization systems that fuse multiple sensor modalities with different data rates to improve model performance and robustness.
  • 3+ years of programming experience in Python and experience with ML frameworks such as PyTorch.


Preferred Qualifications

  • Experience building personalized models for Eye Tracking or other human tracking tasks such as body, face or hand tracking.
  • Experience with large-scale machine learning techniques like semi-supervised learning, weakly-supervised learning and online adaptation of ML models.
  • 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.