At a Glance
- Tasks: Join the Spectacles AR team to develop cutting-edge machine learning models for augmented reality.
- Company: Snap Inc, a leading tech company revolutionising communication through innovative camera technology.
- Benefits: Enjoy competitive pay, health coverage, and a supportive community focused on your well-being.
- Other info: Collaborative office culture with opportunities for personal and professional growth.
- Why this job: Make an impact in AR technology while learning from industry experts in a dynamic environment.
- Qualifications: Pursuing a degree in a technical field with strong Python skills and a passion for machine learning.
The predicted salary is between 20000 - 30000 £ per year.
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.
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.
- Contribute to the Spectacles software organization, which is dedicated to developing the perception and understanding systems necessary for intelligent AR experiences on Spectacles.
- 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).
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).
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.
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!
Machine Learning Engineering Intern in London employer: SNAP
Contact Detail:
SNAP Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineering Intern in London
✨Tip Number 1
Network like a pro! Reach out to current or former Snap employees on LinkedIn. Ask them about their experiences and any tips they might have for landing the internship. Personal connections can make a huge difference!
✨Tip Number 2
Get your hands dirty with projects! Work on personal or open-source projects that showcase your machine learning skills. Having something tangible to discuss during interviews can really set you apart from the crowd.
✨Tip Number 3
Practice your coding skills! Use platforms like LeetCode or HackerRank to brush up on your Python and algorithm knowledge. Being able to solve problems on the spot will impress your interviewers.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in being part of the Snap team!
We think you need these skills to ace Machine Learning Engineering Intern in London
Some tips for your application 🫡
Show Your Passion for AR: When you're writing your application, let your enthusiasm for augmented reality shine through! We want to see how excited you are about the technology and how it can change the way people interact with the world.
Highlight Relevant Skills: Make sure to showcase your skills in Python and any deep learning frameworks you've worked with. We love seeing practical examples of your experience, so don’t hold back on sharing your projects or coursework that relate to machine learning!
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so make sure your ideas come across easily. Use bullet points if it helps to break down your experiences and skills!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at SNAP
✨Know Your Stuff
Make sure you brush up on your computer science fundamentals and machine learning concepts. Be ready to discuss your experience with Python and any deep learning frameworks you've used, like PyTorch or TensorFlow. This will show that you're not just a theory person but can apply your knowledge practically.
✨Showcase Your Projects
Prepare to talk about any relevant projects you've worked on, especially those involving computer vision or natural language processing. Highlight specific challenges you faced and how you overcame them. This gives the interviewers insight into your problem-solving skills and hands-on experience.
✨Ask Smart Questions
Interviews are a two-way street! Prepare thoughtful questions about the Spectacles AR team and their current projects. This shows your genuine interest in the role and helps you understand if it's the right fit for you. Plus, it gives you a chance to engage with your interviewers.
✨Be Ready to Collaborate
Since the role involves working closely with cross-functional teams, be prepared to discuss how you’ve collaborated in the past. Share examples of how you communicated technical ideas clearly and worked towards common goals. This will demonstrate your teamwork skills, which are crucial for success at Snap.