Snap Inc.

London
5,000 Total Employees
Year Founded: 2011

Snap Inc. Offices

Hybrid Workplace

Employees engage in a combination of remote and on-site work.

Our “default together” approach is an 80/20 model where we are asking team members to spend 80% of the time, on average, in the office, with the remaining 20% of the time spent remote.

Typical time on-site: 4 days a week

Global Office Locations

London

London, United Kingdom

Recently posted jobs

10 Hours AgoSaved
Hybrid
London, Greater London, England, GBR
Artificial Intelligence • Cloud • Machine Learning • Mobile • Software • Virtual Reality • App development
Develop machine learning models and computer vision algorithms for photon-efficient, event-driven imaging in AR glasses. Responsibilities include tracking, depth estimation, SLAM, detection, probabilistic modeling, and novel imaging algorithms for ultra-low-light, high-temporal-resolution systems. The role requires collaboration with global hardware and software teams and emphasizes fast, reliable implementation in C++ and Python.
10 Hours AgoSaved
Hybrid
London, Greater London, England, GBR
Artificial Intelligence • Cloud • Machine Learning • Mobile • Software • Virtual Reality • App development
Develop and deploy computer vision and machine learning algorithms for photon-efficient, event-driven imaging in next-generation AR glasses. Responsibilities include tracking, depth estimation, SLAM, detection, probabilistic modeling, and ultra-low-light imaging. The role requires close collaboration with global hardware and software teams and focuses on creating fast, robust, and accurate solutions for wearable devices.
5 Days AgoSaved
Hybrid
London, Greater London, England, GBR
Artificial Intelligence • Cloud • Machine Learning • Mobile • Software • Virtual Reality • App development
Build, deploy, and maintain machine learning models for Snapchat products. Apply ML techniques to real-world problems, contributing across data analysis, training, evaluation, and production deployment. Collaborate with cross-functional teams to launch scalable ML-driven features using modern AI tools and engineering workflows while maintaining high standards for correctness, security, and production quality.