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Snap Inc.

Machine Learning Engineering Intern

Posted An Hour Ago
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Hybrid
London, Greater London, England, GBR
Internship
Hybrid
London, Greater London, England, GBR
Internship
As a Machine Learning Engineering Intern, you will work on scene understanding for AR experiences, prototype machine learning models, and collaborate with teams to integrate your work.
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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. Our fifth-generation Spectacles, powered by Snap OS, showcase how standalone, see-through AR glasses make playing, learning, and working better together.

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!

What you’ll do

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.

Knowledge, Skills & Abilities

  • 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, TensorFlow, 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.

Minimum Qualifications

  • 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.

Preferred Qualifications

  • 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.

"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 that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.

Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success!

Top Skills

Jax
Python
PyTorch
TensorFlow

Snap Inc. London, England Office

London, United Kingdom

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