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, and 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 for augmented reality systems (e.g., face, hand, body)

Requirements

Solid foundation in photon-efficient, event-driven imaging systems
Strong knowledge of computer vision, machine learning, and statistical signal processing
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
BSc/MSc with 3+ years of relevant industry experience in computer vision for photon-efficient or event-driven imaging
Track record of successful projects demonstrating computer vision skills
3+ years of programming experience in C++ and Python (e.g., PyTorch, TensorFlow)

Paid parental leave
Comprehensive medical coverage
Emotional and mental health support programs
Compensation packages that allow employees to share in Snap’s long-term success
“Default together” policy encouraging in-office collaboration 4+ days per week
Commitment to diversity, equal opportunity employment, and inclusive workplace environment