What you'll be doing

Design, train, and deploy machine learning models and computer vision algorithms tailored for photon-efficient, event-driven imaging systems
Contribute to state-of-the-art tracking, depth estimation, SLAM, and ML systems leveraging high-temporal-resolution event-driven data
Develop novel photon-efficient imaging features and algorithms for the next generation of AR Glasses
Research new machine learning and probabilistic techniques to improve speed, noise robustness, and accuracy of computer vision algorithms in photon-efficient, event-driven imaging systems
Work with the computer vision team to deliver next-generation detection technology (e.g., face, hand, body) for augmented reality systems

Requirements

Experience with computer vision for photon-efficient or event-driven imaging systems (e.g., VIO, SLAM, Hand tracking)
5+ years of post-Bachelor’s relevant experience; or Master’s degree in a technical field plus 4+ years of relevant post-grad experience; or PhD in a relevant technical field plus 2 years of relevant experience
Track record of successful projects demonstrating computer vision skills in photon-efficient or event-based imaging
5+ years of programming experience in C++ and Python (e.g., PyTorch, TensorFlow)
Solid foundation in photon-efficient, event-driven imaging systems
Solid foundation in computer vision, machine learning, and statistical signal processing
Strong mathematics background with deep understanding of 3D geometry, probability, statistics, and linear algebra
Ability to transform ideas into clean, fast, and reliable code
Attention to detail, strong desire for quality, and good communication skills
Outstanding problem-solving skills

Paid parental leave
Comprehensive medical coverage
Emotional and mental health support programs
Compensation packages that enable sharing in Snap’s long-term success
Supportive company culture that values diversity and inclusion
Work in a dynamic, innovative environment focused on the future of camera and wearable computing technology