Meta AR/VR Job | Research Scientist, Computer Vision Model Optimization (PhD) | Quest
Job（岗位）: Research Scientist, Computer Vision Model Optimization (PhD) | Quest
Type（岗位类型）: Computer Vision
Citys（岗位城市）: Burlingame, CA
Date（发布日期）: 2023-6-12
Summary（岗位介绍）
Meta Reality Labs is the global leader in the development of virtual reality (VR), augmented reality (AR), and mixed reality (MR) systems. We are looking for innovative and self-motivated Research Scientists to drive advancements in the field of VR/AR and MR. Join our exceptional team and play a pivotal role in developing cutting-edge deep learning algorithms and efficient models tailored specifically for these crucial areas. Your expertise will be instrumental in shaping immersive experiences and revolutionizing the way we interact with the real and virtual worlds.
In this role, your primary responsibility will be to create innovative solutions that optimize computational efficiency while delivering exceptional performance. You will develop advanced techniques in model compression, including quantization, pruning, neural architecture search, and hardware-aware model optimization, to enable the deployment of powerful AI models on resource-constrained devices.
Additionally, you will have the opportunity to tackle captivating challenges and develop state-of-the-art algorithms that enhance scene understanding, human understanding, avatars, color passthrough and generative AI in the context of AR/VR and MR systems. Your contributions will elevate VR/AR and MR to unprecedented levels. Strong expertise in building efficient models, training methods and model compression for computer vision is preferred for this position.
Qualifications（岗位要求）
Currently has, or is in the process of obtaining, a PhD degree in Machine Learning, Artificial Intelligence, Computer Vision, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
Hands-on experience in deep learning algorithms and techniques, e.g., convolutional neural networks (CNN), transformers, quantization, data efficient learning, or similar.
Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.
3+ years experience with deep learning software libraries such as PyTorch or TensorFlow.
Description（岗位职责）
Develop efficient deep learning models for problems in computer vision, e.g., deep learning solutions for scene understanding, 3D reconstruction, object detection, segmentation, depth, avatars, view synthesis and neural rendering.
Develop deep learning model optimization algorithms and infrastructure to enable efficient model deployment on AR/VR and MR devices.
Build methods for efficient use of data for training models, leveraging semi-supervised learning techniques and innovate on techniques for core set selection from massive datasets.
Optimize models on hardware to achieve the best performance given various real time latency and power constraints.
Lead and contribute to cutting-edge research that results in industry-leading tech demos and/or publications.
Collaborate with cross-disciplinary research and engineering teams.
Additional Requirements（额外要求）
Experience in neural architecture search, quantization, model/accelerator co-design and co-optimization, or related fields.
Experience in diffusion models, avatars, 3D generation, or related fields.
Experience on data efficient learning, domain adaptation, semi-supervised learning, etc.
Experience in related fields of 3D computer vision, scene and human understanding, and multi-sensor fusion.
Experience working and communicating cross-functionally in a team environment.
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as CVPR, ICLR, NeurIPS, ICCV, ICML, ECCV, or similar.
Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).
