Senior Signal Processing Engineer (Embedded Machine Learning)

Key Skills

Aartificial intelligence, C Programming, Data science techniques, Keras software library, Machine learning techniques, Product Management, Python Programming, PyTorch, Signal processing, Software Development, TensorFlow

Job Description

As a Senior Signal Processing Engineer at WHOOP, you will be part of a cross-functional team composed of DSP, WHOOP Labs, Firmware, and Data Science. You will work on the core, fundamental features at WHOOP. This role will be tasked with solving the incredibly difficult technical problem of obtaining physiological information from noisy sensor data, as well as optimizing the on-strap computation.

RESPONSIBILITIES:

• Partner with firmware and Data Science teams to deploy AI models on edge devices
• Optimize models for inference on edge devices and analyze battery consumption patterns
• Work with the rest of the Signal Processing team to bring insight and algorithms to personalize the calculations on the device
• Develop innovative and creative algorithms to improve the performance under edge calculation restrictions
• Cooperate in research and explore new features
• Participate in software development, debugging, and validation in order to ensure code and results are production-ready
• Build, train and test machine learning models for big data on the edge
• Collaborate with Product Managers to translate member needs to ML solutions

QUALIFICATIONS:

• Bacheller or Master's degree in applied mathematics, statistics, electrical engineering, biomedical engineering, or related field
• 4+ years of industry or research experience in signal processing and/or machine learning
• Experience developing and implementing AI solutions for real-time processing on edge devices
• Experience collaborating with cross-functional teams to understand requirements and design efficient edge computing solutions
• Experience with biosensor systems and analyzing biomedical data
• Expertise in C and/or Python
• Knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, etc.
• Knowledge of statistical method and design of clinical studies
• Strong oral and written interpersonal skills
• Experience with code and battery optimization on the edge is desirable.
• Innovative, knowledgeable, and able to adapt to changing requirements while still producing quality reports with tight deadlines