About the job
Reality Labs at Meta is seeking Research Scientist Manager with experience in machine learning 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.
Responsibilities
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. Work across organizational boundaries to solve our biggest problems
Qualifications
Minimum
8+ years of experience working autonomously to design, execute, interpret, and present ML research outcomes
4+ years of experience as technical lead for a project of 4 or more individuals
2+ years of direct management experience, managing Researchers or Engineers
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 bringing machine learning-based products from research to production
Experience with interdisciplinary and/or cross-functional collaboration
Experience with large scale cluster computing
Preferred
Experience with real-time signal processing and/or human-computer interaction
Proficiency with quantitative methods (mathematics, statistics) and experience learning new technical knowledge and skills rapidly
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)
Experience with scientific communication tools (Jupyter, Matplotlib)