Machine Learning Engineering Intern

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’s three core products are Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world;
Lens Studio, an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles.

The Spectacles team is pushing the boundaries of technology to bring people closer together in the real world.

Snap’s camera supports real friendships through visual communication, self expression and storytelling. Moving forward, our camera will play a transformative role in how people experience the world around them, combining what they see in the real world, with all that’s available to them in the digital world.

We are looking for a Machine Learning Engineering Intern to join the Spectacles AR engineering team at Snap Inc!

Join the Spectacles AR team in the London, UK office for a 13-week Summer 2026 Machine Learning Engineering Internship. As an intern, you will contribute to the Spectacles software organization, which is dedicated to developing the perception and understanding systems necessary for intelligent AR experiences on Spectacles.

Additionally you will:

• Work on a technical project that aligns with Spectacles product and research needs, focused on scene understanding for AR experiences.
• Prototype, train, and evaluate machine learning models for computer vision and multimodal understanding, using Python and modern deep learning frameworks.
• Contribute to models, tooling, and algorithms in geometric scene understanding, 3D reconstruction, semantic scene understanding, visual localisation, and connecting scene understanding to language for richer, more semantic AR interactions.
• Partner closely with your mentor and teammates across Spectacles software and other cross-functional teams to integrate your work into production‑facing systems.
• Learn and apply new software engineering and machine learning skills in a fast‑paced, collaborative environment.

• Strong computer science fundamentals and problem‑solving skills.
• Proficiency in Python for data processing, model development, and experimentation.
• Familiarity with at least one deep learning framework (e.g. PyTorch, Tensor Flow, or JAX).
• Understanding of core concepts in machine learning and at least one of:
  • Computer Vision (e.g. image classification, detection, segmentation, depth estimation, optical flow, 3D geometry), or
  • Natural Language / LLMs (e.g. sequence modeling, transformers, language model fine‑tuning, vision‑language models).
• Ability to understand, debug, and improve existing code as well as develop new algorithms using advanced computer vision and machine learning techniques.
• Ability to collaborate with other engineers and cross‑functional partners, and communicate technical ideas clearly.
• Comfortable working in a Linux‑based development environment.

• Currently enrolled in a BS, MS program in a technical field such as Computer Science, Electrical/Computer Engineering, Mathematics, or a related discipline, with a graduation date no sooner than December 2026.
• Graduating between December 2026 and Spring 2027.
• Must be able to start in office in May or June 2026 for a 13‑week internship.

• Coursework or hands‑on project experience in machine learning or deep learning.
• Experience writing, documenting and debugging high quality code in Python.
• Experience with standard developer practices (version control, rigorous testing, documentation standards).

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

The “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…