R&D Machine Learning Scientist
Eight SleepSalary not specified
- Full Time
- Junior (1 to 2 years)
Not SpecifiedCompensation
Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Biotechnology, Healthcare, AI & Machine LearningIndustries
Requirements
Candidates should possess an MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, or a related field, along with 5+ years of experience developing signal processing and machine learning algorithms for time-series data. Proficiency in Python and C/C++ is required, alongside experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, and a deep understanding of physiological signal processing and time-series modeling.
Responsibilities
The Senior Sensor Algorithms Engineer will develop and optimize signal processing and machine learning algorithms to extract physiological metrics from raw sensor data, analyze large-scale wearable sensor datasets to improve accuracy and efficiency, collaborate with data scientists and engineers to translate research into production-ready features, implement scalable algorithms for real-time and offline processing, work closely with firmware and hardware teams for integration, and validate algorithms through rigorous testing. They will also stay current with advancements in machine learning and physiological modeling, bringing innovative ideas to WHOOP, and deploying algorithms in production environments.
Skills
Python
C/C++
TensorFlow
PyTorch
scikit-learn
Signal Processing
Machine Learning
Time-series modeling
Data Analysis
Algorithm Development
Whoop
Wearable fitness tracker with personalized insights
About Whoop
WHOOP offers a fitness membership that focuses on improving personal health and performance through a wearable device called the WHOOP Strap 3.0. This device continuously collects physiological data, including heart rate, sleep patterns, and recovery levels, to provide users with personalized recommendations on their daily activity, sleep needs, and readiness for performance. Unlike many competitors, WHOOP operates on a subscription model, where users pay a fee to access the membership, which includes the device and continuous insights through the WHOOP app. This model not only provides a steady revenue stream but also fosters a strong community among users, encouraging engagement through teams, challenges, and social features. The goal of WHOOP is to help users optimize their health and performance while minimizing injury risk.
Key Metrics
Boston, MassachusettsHeadquarters
2012Year Founded
$393.7MTotal Funding
SERIES_FCompany Stage
Data & Analytics, HealthcareIndustries
501-1,000Employees