Research Scientist, Wearables AI

Meta•New York, NY

5d•$184,000 - $257,000

About The Position

Meta is seeking a Research Scientist with experience in product-focused signal processing and machine learning to help us create novel wearable sensors and algorithms to power the next generation of neuromotor interfaces and augmented reality systems. Work to reduce latency and friction between user intent and action through improved sensing and algorithms.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Ph.D. in machine learning, AI, computer science, electrical engineering, statistics, applied mathematics, data science, signal processing, optimization or related technical fields
Record of publication demonstrating research excellence
6+ years of experience in signal processing, machine learning, or sensor development in an industry R&D setting
Research-oriented software engineering skills (SciPy ecosystem/ML tools, MATLAB)

Nice To Haves

Experience working in consumer hardware engineering
Experience building user interaction systems and/or working in human-computer interaction
Experience shipping consumer hardware products at large scale
Demonstrated experience of statistical signal processing skills
Statistically grounded design of test methods for evaluating sensor hardware performance in lab and factory (e.g. aggressor studies and simulations, hardware phantoms)
Experience with human-in-loop sensor development (e.g. biosensors or novel input devices such as electrocardiography (EKG/ECG), photoplethysmography (PPG), capacitive sensing, electromyography, etc)
Experience defining research/technical direction for a team and to support the work of a small group of researchers toward those goals

Responsibilities

Collaborating with hardware and machine learning teams to define sensor performance requirements as well as characterize the dependencies between sensors and overall system performance
Exploring and designing new sensing architectures and characterizing their impact on the downstream ML models and their requirements
Building simulation tools and use them to explore sensor performance
Developing methodologies and tools to simulate biophysical signals and their approximations