At WHOOP, we're on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.

 

As a Signal Processing Engineer at WHOOP, you will be part of a cross-functional team comprised of DSP, Soft Goods, 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 and enhancing diagnostic tools and control methodologies.

RESPONSIBILITIES:

• Collaborate closely with Data Science and Research teams to enhance user metrics by developing innovative and creative algorithms.
• Monitor and ensure the proper functioning of algorithms across our diverse user population, addressing any issues related to data and data quality.
• Validate wearable technology in clinical settings, analyze biomedical data, and prepare comprehensive reports for cross-functional teams.
• Participate in software development, debugging, and validation processes to ensure code and results are production-ready.
• Contribute to ongoing research efforts and explore new features to improve the overall performance of our products.
• Utilize expertise in signal processing and time-series analysis to analyze biosensor systems and optimize their performance.
• Create and implement mathematical models and machine learning algorithms for processing big data and extracting valuable insights.

QUALIFICATIONS:

• Bachelor's or Master's degree in applied mathematics, statistics, electrical engineering, biomedical engineering, or a related field.
• A minimum of 2 years of industry or research experience in signal processing and/or machine learning.
• Strong experience with biosensor systems and analyzing biomedical data.
• Proficiency in C and/or Python programming languages.
• Knowledge of adaptive signal processing and time-series analysis.
• Solid understanding of statistical methods and experience in designing clinical studies.
• Familiarity with machine learning libraries such as scikit-learn, Tensorflow, PyTorch, Keras, etc.
• Excellent communication skills, both written and oral, to effectively convey complex technical concepts to diverse teams.
• Demonstrated ability to think innovatively and adapt to changing requirements while consistently producing high-quality reports within tight deadlines.