Research Scientist - Neural Interfaces
- Woodinville, WA
Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities - we're just getting started.
Facebook Reality Labs is seeking a Research Scientist to help us unleash human potential by eliminating the bottlenecks between intent and action. To achieve this, we're building a practical neural interface drawing on the rich motor neuron signals that can be measured non-invasively with neuron-level resolution. Our research lies at the intersection of computational neuroscience, machine learning, signal processing, statistics, biophysics, motor learning, perceptual psychophysics, and human-computer interaction. We're looking for people who want to shape the future of this technology and are excited about joining our collaborative research team that has grown out of the acquisition of CTRL-labs.
- Plan and execute cutting-edge applied research to advance neural interface capabilities.
- Collaborate with engineering teams to translate fundamental scientific knowledge into new technology.
- Use quantitative research methods to define, iterate upon and advance key areas of our research agenda.
- Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
- PhD in computational neuroscience, machine learning, physics, computer science, or related fields.
- Experience with research-oriented implementation skills, including fluency with libraries for scientific computing (e.g. scipy ecosystem) and machine learning (e.g. scikit-learn, PyTorch, TensorFlow).
- Experience with quantitative skills (mathematics, statistics) and experience learning new technical knowledge and skills rapidly.
- Experience working independently to design, execute, interpret, and present research studies.
- Technical and non-technical communication skills.
- Experience with cross-domain and cross-culture collaboration.
- Experience in signal processing and familiarity with real-time signals.
Back to top