Machine Learning Engineer
Vor 6 Tagen
Snap Inc. is a technology company that empowers people to express themselves, live in the moment, learn about the world, and have fun together. Our three core products are Snapchat, an augmented reality platform that powers AR across Snapchat and other services, and our AR glasses.
About the RoleWe are seeking a highly skilled Machine Learning Engineer to join our Research & Development team in Vienna, Austria. As a key member of our team, you will work on state-of-the-art machine learning technologies to straddle the boundaries between the real and the virtual world with the next generation of Snap's wearable computing devices.
Key Responsibilities- Develop novel technologies for the next generation of wearable AR devices.
- Explore and advance state-of-the-art machine learning and computer vision algorithms.
- Develop and deploy machine learning models.
- Collaborate with our cross-functional engineering and research teams in computer vision, machine learning, and graphics.
- Deep understanding of machine learning principles, solutions, and frameworks to develop networks and models for computer vision tasks.
- Ability to understand, debug, and improve existing code as well as develop new algorithms using advanced computer vision and machine learning techniques.
- Strong communications and interpersonal skills.
- A genuine passion for learning new things and helping colleagues improve.
- Bachelor's degree in a technical field such as computer science, mathematics, or equivalent experience.
- 2+ years of research or engineering experience with machine learning approaches, in one or more of the following areas: object detection, hand/body tracking, object pose tracking, scene understanding (segmentation, classification), neural scene representation.
- Experience with machine learning frameworks (PyTorch, TensorFlow, etc.), as well as with cloud environments (GC, AWS, etc.).
- Experience with software development in Python or C++.
- MSc in related field (Computer Vision, Machine Learning).
- Experience with neural network optimization (pruning, quantization, distillation) to deploy efficient models to resource-constrained devices.
- Experience in geometric computer vision such as multi-view 3D reconstruction, SLAM, tracking, depth estimation, etc.
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