Research Scientist, Neuromotor Interfaces

Reality Labs at Meta is seeking Research Scientists with experience in product-focused 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 interface drawing on the rich neuromotor signals that can be measured non-invasively via surface electromyography (EMG) with single motor neuron resolution. This technology could become one of the main pillars for interaction with virtual and augmented worlds.We are a multi-disciplinary team of researchers investigating the nature of human neuromotor signals, 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.

• Designing experiments to evaluate and improve the performance and failure modes of consumer-grade, wrist-worn input devices
• Designing human data collection protocols to generate large datasets for model training and evaluation of novel behaviors under realistic conditions of use
• Performing ML experiments to inform data requirements and drive model performance improvements
• Collaborating with hardware and machine learning teams to develop data-based mitigations to in-field signal aggressors
• Generating insights and tools to improve the efficiency and quality of human data collection studies
• Guaranteeing the quality and validity of experimental datasets

• Research-oriented software engineering skills, including fluency with libraries for scientific computing (e.g. SciPy ecosystem)
• Proficiency with quantitative methods (mathematics, statistics) and experience acquiring new technical knowledge and skills rapidly

• 2+ years of experience after PhD working independently to design, execute, interpret, and present research studies
• Experience in the analysis and modeling of high dimensional time series, such as neural signals, other physiological signals, audio recordings, robotic sensory signals, financial time series, video, or other sensor modalities
• Experience in deep learning or probabilistic graphical models. Fluency with libraries for deep learning and machine learning (e.g., PyTorch, TensorFlow, Scikit-learn)
• Experience in software engineering in industry
• Experience bringing machine learning-based products from research to production