Senior Signal Processing Engineer

Whoop Whoop · Consumer · Boston, MA · Sensor Intelligence

Senior Signal Processing Engineer at Whoop to design and implement algorithms for physiological information extraction from biosensor data, optimize on-device computation, and deploy AI/ML models on edge devices. Responsibilities include optimizing models for real-time inference, battery consumption, personalizing calculations, researching innovative algorithms under edge-computation constraints, contributing to software development, building/training/testing ML models, and translating member needs into ML-based solutions.

What you'd actually do

  1. Partner with Firmware and Data Science teams to deploy artificial intelligence and machine learning models on edge devices.
  2. Optimize models for real-time inference on edge devices, including analysis and improvement of battery consumption patterns.
  3. Collaborate with the Signal Processing team to develop algorithms that personalize calculations based on member data.
  4. Research and design innovative algorithms to improve performance under edge-computation constraints.
  5. Contribute to software development, debugging, and validation to ensure production-ready code and reliable results.

Skills

Required

  • Signal processing algorithm design and implementation
  • Machine learning model deployment on edge devices
  • Real-time inference optimization
  • Battery consumption analysis and improvement
  • Personalization algorithms
  • Edge computation constraint optimization
  • Software development, debugging, and validation
  • Machine learning model training and testing
  • Translating member needs into ML solutions
  • C, C++, Python or MATLAB
  • scikit-learn, Tensorflow, PyTorch or Keras
  • Statistical methods
  • Clinical study design

Nice to have

  • Master's degree in Computer Science, Applied Mathematics, Electrical Engineering, Biomedical Engineering, or related field (or foreign degree equivalent)

What the JD emphasized

  • At least 4 years of experience developing and implementing AI solutions for real-time processing on edge devices
  • At least 4 years of experience with biosensor systems and analyzing biomedical data
  • At least 4 years of experience with ML libraries such as scikit-learn, Tensorflow, PyTorch or Keras
  • At least 4 years of experience with code and battery optimization on the edge

Other signals

  • Deploy AI/ML models on edge devices
  • Optimize models for real-time inference on edge devices
  • Build, train, and test ML models for large-scale data processing on edge devices