Computer Vision & Machine Learning Engineer, Photon-Efficient Event-Driven Imaging

London, United Kingdom

Full time

R0046959

Snap Inc () is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.

The Company operates Snapchat () , a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc. () , a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji () , Saturn, and other digital services.

Snap’s Spectacles unit is a world-class Research & Development team, working together to pave the way for the future of the camera and wearable computing. We’re working on cutting-edge capture and AR solutions and are inventing new ways in which computing will be overlaid on the world around us.

The Spectacles Computer Vision Team works to solve some of the most important challenges in Computer Vision, Machine Learning, and Augmented Reality. As a Computer Vision & Machine Learning Engineer on this team, you’ll work on solving tough challenges in ultra-low-light and high-temporal-resolution imaging, developing novel algorithms for photon-efficient, event-driven imaging systems for the next generation of wearable devices. We work on wearables and look to push the boundaries of what is possible.

Working from our London office, you will be collaborating closely with other Snap hardware and software teams around the world.

• 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 our computer vision team to deliver next-generation detection (such as face, hand, body etc.) technology for augmented reality systems

• Solid foundation in photon-efficient, event-driven imaging systems

• Solid foundation in computer vision, machine learning, and statistical signal processing

• Solid math 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

• 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 + 4+ years of relevant post-grad experience; or PhD in a relevant technical field + 2 years of relevant experience

• Track record of successful projects showing computer vision skills in photon efficient or event-based imaging

• 5+ years of programming experience in C++ and Python (e.g. PyTorch, Tensor Flow)

If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information () .

"Default Together" Policy at Snap:
At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week.

At Snap, we believe…