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- Tasks: Develop cutting-edge machine learning and computer vision technologies for AR devices.
- Company: Join Snap Inc, a tech leader revolutionising communication through innovative camera technology.
- Benefits: Enjoy parental leave, medical coverage, mental health support, and a chance to share in Snap's success.
- Why this job: Be part of a dynamic team pushing the boundaries of AR technology and enhancing real-world connections.
- Qualifications: Bachelor's degree in a relevant field; experience in computer vision/machine learning is essential.
- Other info: Work in London with a collaborative team, embracing diversity and equal opportunity.
The predicted salary is between 36000 - 60000 £ 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, Lens Studio, and 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.
We are looking for a Machine Learning Engineer to join the AR team in London, UK. In this role, you will work on state-of-the-art machine learning and computer vision technologies to develop next-generation wearable AR devices. You will collaborate with our global teams from our London office.
What you\’ll do:
- Develop and productise novel technologies for wearable AR devices.
- Advance state-of-the-art machine learning and computer vision algorithms.
- Develop and deploy machine learning models.
- Collaborate with cross-functional teams in computer vision, machine learning, and AR engineering.
Knowledge, Skills & Abilities:
- Deep understanding of machine learning principles, solutions, and frameworks for computer vision tasks.
- Ability to debug and improve existing code and develop new algorithms using advanced techniques.
- Strong communication and interpersonal skills.
- Passion for learning and helping colleagues improve.
Minimum Qualifications:
- Bachelor\’s Degree in a relevant technical field or equivalent practical experience.
- Extensive post-Bachelor\’s experience in computer vision/machine learning or equivalent advanced degrees with experience.
- Experience developing machine learning models in areas like scene understanding, depth estimation, or visual localization.
Preferred Qualifications:
- MSc/PhD in related fields.
- Experience integrating ML models into AR solutions.
- Experience in neural network optimization for resource-constrained devices.
- Knowledge of geometric computer vision techniques such as SLAM, VIO, multi-view reconstruction.
- Proficiency in C++ software development.
If you have a disability or special need requiring accommodation, please let us know.
At Snap, we believe in a \”default together\” policy, requiring team members to work in the office 4+ days a week. We are committed to diversity and equal opportunity employment, welcoming applicants regardless of race, religion, disability, gender, or other protected classes.
Our benefits include parental leave, comprehensive medical coverage, mental health support, and opportunities to share in Snap\’s success.
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Computer Vision Engineer employer: Snapchat
Contact Detail:
Snapchat Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Computer Vision Engineer
✨Tip Number 1
Familiarise yourself with the latest advancements in computer vision and machine learning. Follow relevant blogs, attend webinars, and participate in online forums to stay updated on cutting-edge technologies that Snap Inc is likely to be using.
✨Tip Number 2
Network with professionals in the AR and machine learning fields. Attend industry conferences or local meetups to connect with people who work at Snap or similar companies. Building relationships can often lead to referrals or insider information about job openings.
✨Tip Number 3
Showcase your skills through personal projects or contributions to open-source initiatives related to AR and computer vision. Having a portfolio that demonstrates your ability to develop and deploy machine learning models will make you stand out to the hiring team.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and algorithm problems, especially those related to machine learning and computer vision. Use platforms like LeetCode or HackerRank to sharpen your skills and get comfortable with problem-solving under pressure.
We think you need these skills to ace Computer Vision Engineer
Some tips for your application 🫡
Understand the Role: Before applying, make sure to thoroughly understand the responsibilities and requirements of the Computer Vision Engineer position at Snap Inc. Familiarise yourself with their products and how your skills align with their needs.
Tailor Your CV: Customise your CV to highlight relevant experience in machine learning and computer vision. Emphasise any projects or roles that demonstrate your ability to develop and deploy machine learning models, especially in AR contexts.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AR technology and your understanding of Snap's mission. Mention specific experiences that relate to the job description, such as developing algorithms or working with cross-functional teams.
Showcase Your Skills: In your application, provide examples of your technical skills, particularly in C++ and machine learning frameworks. If you have experience with geometric computer vision techniques or neural network optimisation, be sure to include that as well.
How to prepare for a job interview at Snapchat
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning and computer vision technologies. Highlight specific projects where you've developed or deployed models, especially in areas like scene understanding or depth estimation.
✨Demonstrate Collaboration
Since the role involves working with cross-functional teams, share examples of how you've successfully collaborated with others in previous roles. Emphasise your communication skills and your ability to work well in a team environment.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving abilities. Brush up on debugging techniques and be ready to discuss how you would improve existing algorithms or develop new ones using advanced techniques.
✨Express Your Passion for AR and Learning
Convey your enthusiasm for augmented reality and your commitment to continuous learning. Discuss any relevant personal projects or research that demonstrate your passion and willingness to help colleagues improve their skills.
