Meta AR/VR Job | AI Research Scientist, Neuromotor Interfaces, ML & Signal Processing
Job（岗位）: AI Research Scientist, Neuromotor Interfaces, ML & Signal Processing
Type（岗位类型）: Artificial Intelligence | Machine Learning, Research
Citys（岗位城市）: Burlingame, CA | New York, NY
Date（发布日期）: 2022-10-28
Summary（岗位介绍）
Reality Labs at Meta is seeking AI Research Scientists with experience in machine learning and signal processing research to advance our pioneering work in neuromotor interfaces, which has grown out of the acquisition of CTRL-labs. We’re building a practical neural interface drawing on the rich neuromotor signals that can be measured non-invasively with single motor neuron resolution. This technology will become one of the main pillars for interaction with the virtual and augmented world.
We are a multi-disciplinary team of researchers investigating the nature of human neuromotor signals (i.e., electromyography or EMG), developing novel signal processing and machine learning methods to infer a user’s intent, and creating novel interaction techniques and user experiences. Help us unleash human potential by removing the bottlenecks between user intent and action.
EMG signals are similar in many ways to other neural signals and audio signals and can be analyzed using many of the same approaches. For example, the task of transcribing characters typed on a keyboard from wrist-based EMG is closely analogous to that of automatic speech recognition. We’re looking for people who want to shape the future of this technology and explore this exciting new territory with us.
Qualifications（岗位要求）
PhD in one of the following fields: deep learning, artificial intelligence, machine learning, computer science, robotics, computer vision, computational neuroscience, signal processing, speech and language technologies, or related fields.
Research-oriented software engineering skills, including fluency with libraries for scientific computing (e.g. SciPy ecosystem) and machine learning (e.g., PyTorch, TensorFlow, Scikit-learn, Pandas).
Proficiency with quantitative methods (mathematics, statistics) and experience learning new technical knowledge and skills rapidly.
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）
Build cutting-edge machine learning and signal processing models (event detection, sequence-to-sequence, signal separation, time series regression, etc.) to advance neuromotor interface capabilities.
Collaborate with engineering and Human-Computer Interactions (HCI) teams to deploy models that leverage fundamental scientific knowledge into new technology and user experiences.
Use quantitative research methods to define, iterate upon and advance key areas of our research agenda.
Develop research-grade code for deployment in research prototypes.
Set technical direction for a project of 2-3 researchers and engineers.
Additional Requirements（额外要求）
5+ years of experience working autonomously to design, execute, interpret, and present research studies.
2+ years of experience as technical lead for a project of 2-3 individuals.
Experience bringing machine learning-based products from research to production.
Experience with interdisciplinary and/or cross-cultural collaboration.
Experience in the analysis and modeling of high dimensional time series, such as neural signals, multi-channel audio recordings, multi-modal/multi-sensor signals, robotic sensory signals, financial time series, video, or other sensor modalities.
Experience with speech enhancement, speech recognition, speech synthesis, source separation, audio event detection and classification, audio scene recognition, microphone arrays, beamforming, or related topics.
Experience with real-time signal processing and/or human-computer interaction.
Experience with large scale cluster computing for machine learning modeling.
Experience developing end-to-end ML pipelines, including: dataset preprocessing, model development and evaluation, and software integration.
Experience in software engineering in industry.
Experience with scientific communication tools (Jupyter, Matplotlib).
