Meta AR/VR Job | Research Scientist, Human-Computer Co-adaptive Interfaces (PhD)
Job（岗位）: Research Scientist, Human-Computer Co-adaptive Interfaces (PhD)
Type（岗位类型）: Research
Citys（岗位城市）: Burlingame, CA
Date（发布日期）: Before 2021-12-14
Summary（岗位介绍）
In this position, you will work with an interdisciplinary team of domain experts in embodied artificial intelligence (AI), human–computer interaction, computer vision, cognitive and perceptual science, and sensing and tracking on problems that contribute to creating human-machine co-adaptive interfaces which enable easy discoverability and human learning of novel input methods, along with online adaptation of input & action recognition models. The position will involve building models that integrate multimodal data sources—including electromyography (EMG), video, and other biosignals from wrist-wearable inputs and other sensing methods—to build personalized models for recognizing input commands to future augmented-reality devices. These models will leverage large-scale real-world data sets and the scale of Facebook machine-learning infrastructure, and will be deployed into AR/VR prototypes to uncover research questions on the path to the next era of human-centered computing.
Qualifications（岗位要求）
Currently has, or is in the process of obtaining, a PhD degree in the field of deep learning, artificial intelligence, machine learning, computer science, computational neuroscience or related technical field
Demonstrated track record in developing scalable, robust systems for training deep-learning models
3+ years of experience in PyTorch or equivalent framework
Interpersonal experience: cross-group and cross-culture collaboration
Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
Description（岗位职责）
Formulate and evaluate hypotheses from ideation all the way through implementation and demonstration of live online experimental results
Design and build new datasets for exploring methods for developing new input interfaces using novel sensor system prototypes
Explore applied machine learning methods starting from building 0-to-1 baselines on novel problems & datasets through progression towards modern machine learning methods
Leverage advances in traditional machine learning domains such as Speech, online learning, active learning, reinforcement learning, and others for exploring improvements to decoding novel sensor data
Additional Requirements（额外要求）
Research experience with Automatic Speech Recognition, Machine Translation, and/or Text-To-Speech
Experience spanning hypothesis formulation, dataset preprocessing, training, and evaluation of new algorithms to implementations of reusable Python modules
Experience with deploying machine-learning/AI systems in closed-loop systems
Experience with joint hardware-software development and associated rapid prototyping
Experience with biosignals, body-machine interfaces, neural analysis, signal processing, or related fields
Experience working in a modern software development environment, including: unit testing, source control, and continuous integration
