Job Information
Meta Research Scientist, Neural Interfaces in Burlingame, California
Summary:
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.
Required Skills:
Research Scientist, Neural Interfaces Responsibilities:
Plan and execute cutting-edge applied research to advance neuromotor interface capabilities.
Design experiments to collect neuromotor and interaction data and evaluate human and model performance.
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.
Set technical direction for a project of 2-3 researchers and engineers.
Minimum Qualifications:
Minimum Qualifications:
PhD in Computer Science, Electrical Engineering or relevant fields.
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.
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.
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.
Preferred Qualifications:
Preferred Qualifications:
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 with scientific communication tools (jupyter, matplotlib).
Experience bringing machine learning-based products from research to production.
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at peer-reviewed AI conferences (e.g. NeurIPS, CVPR, ICML, ICLR, ICCV, ACL, and ICASSP).
Public Compensation:
$177,000/year to $251,000/year + bonus + equity + benefits
Industry: Internet
Equal Opportunity:
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
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