Meta AR/VR Job | Research Scientist, Affective and Behavioral Computing
Job（岗位）: Research Scientist, Affective and Behavioral Computing
Type（岗位类型）: Artificial Intelligence | Research
Citys（岗位城市）: Redmond, WA
Date（发布日期）: Before 2021-12-14
Summary（岗位介绍）
At Facebook Reality Labs Research, our goal is to explore, innovate, and design novel interfaces and hardware for virtual, augmented, and mixed reality experiences. We are driving research towards a vision of an always-on augmented reality device that can enable high-quality contextually relevant interactions across a range of complex, dynamic, real-world tasks in natural environments; to achieve this goal, our team draws on methods and knowledge from artificial intelligence, machine learning, computer vision, and human–computer interaction. We are looking for a skilled and motivated researcher with expertise in affective computing, behavioral computing, cognitive science, social robotics, personalized and assistive multimodal machine learning, or related fields to join our team. More broadly, the chosen candidate will work with a diverse and highly interdisciplinary team of researchers and engineers and will have access to cutting edge technology, resources, and testing facilities.
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 biosensing on problems that contribute to creating human-centered contextual interactive systems. The position will involve leading a research team in pioneering novel approaches for delivering closed-loop optimal assistance for human activities that draw heavily from ‘internal’ affective/emotive/cognitive contextual states, targeting deployment in future augmented reality devices and associated wearable 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）
PhD degree in computer science, artificial intelligence, machine learning, control theory, or related technical field
Demonstrated track record in defining and leading research in affective and behavioral computing, including estimation, recognition, and forecasting of affect, health, and/or wellbeing
Demonstrated experience with methods and algorithms from sequential decision making, including those arising in optimal control, dynamic programming, reinforcement learning, or related areas
3+ years of experience in leading research teams, including mentorship and management of PhD students, postdocs, and research staff
3+ years of experience in at least one deep-learning software library (e.g., PyTorch, Caffe2, TensorFlow, Keras, Chainer). This experience should include formulation, training, and evaluation of new algorithms and writing reusable Python modules
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）
Develop and execute a cutting-edge research program with interdisciplinary collaborators aimed at developing closed-loop optimal assistance for human activities that draw heavily from ‘internal’ affective/emotive/cognitive contextual states
Develop tasks, data-collection strategies, modeling approaches, and evaluation criteria to deliver on research program objectives, with a particular focus on approaches that leverage wearable devices with multimodal biosensors
Work collaboratively with other research scientists to develop novel solutions and models in service of contextualized AI for augmented reality
Mentor MS/PhD interns and postdocs and collaborate closely with cross-organizational collaborators and external academic groups to advance our research objectives
Additional Requirements（额外要求）
Demonstrated experience conducting research that leverages multimodal data collected from wearable devices
Demonstrated experience leading a team of researchers toward executing on a complex technical goal in a cross-functional setting
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 NeurIPS, ECCV/ICCV, ICCP, ICML, 3DV, BMVC, or SIGGRAPH
Familiarity with ideas from few-shot learning, multimodal representation learning, transfer learning, and recommender systems
