Research Scientist, Computer Vision - Generative AI

Meta is seeking a research scientist to join our Generative AI organization. In this role, you will have the opportunity to make a significant contribution to the field of image generation by developing novel models and algorithms. These models will have a direct impact on Meta’s family of apps with 3 billion plus users. You will design and develop cutting-edge computer vision models and deploy... models to production to solve real world problems. At Meta, you work alongside and learn from top minds in the field and have access to uniquely large scale computation resources. Join us for this exciting and rewarding role and we are hiring in multiple locations.
Research Scientist, Computer Vision - Generative AI Responsibilities
• Designing and developing computer vision models in Generative AI, focusing on image generation, transformation and editing
• Consistently and sustainably advance the state of AI, including setting and executing against roadmaps for 6-month plus timeframes
• Work towards long-term ambitious research goals, identifying intermediate milestones
• Lead other researchers and engineers to solve critical problems and provide mentorship to members
• Work and collaborate with cross-functional teams, build relationships with stakeholders
• Define use cases and develop methodology and benchmarks to evaluate different approaches
Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• Ph.D degree in Computer Science, Computer Engineering and other related fields
• 5+ years of Industry or academic research experience on cutting edge computer vision technologies
• Experience with modern computer vision techniques and deep learning algorithms
• Published in computer visio
• n venues, such as CVPR, ECCV, ICCV, NeurIPS, SIGGRAPH etc.
• Fluent in Python and PyTorch
Preferred Qualifications
• Experience with large-scale distributed system and working with large amounts of data
• Experience with bringing research to production on real-world applications
• Experience with generative models, such as diffusion models and GAN models

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