Whoop2mo ago

Sensor Intelligence Engineer II (Tools & Validation)

Bostonmid

OtherSensor Intelligence Engineer

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Overview

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.

Technical Tools

OtherSensor Intelligence Engineer

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 Sensor Intelligence Engineer at WHOOP, you will be part of a cross-functional team composed of DSP, Apparel & Accessories, WHOOP Labs, Firmware, and Data Science. You will work on the core, fundamental features at WHOOP. This role is tasked with solving the technically challenging problem of extracting physiological information from noisy sensor data, while enhancing diagnostic tools and control methodologies.

Collaborate closely with Data Science and Research teams to enhance user metrics by developing innovative and creative algorithms.

Partner with WHOOP Labs to plan data collections, define acceptance criteria, and assess algorithm readiness for real-world deployment.

Validate wearable technology in clinical settings, analyze biomedical data, and prepare comprehensive reports for cross-functional teams.

Monitor and ensure the proper functioning of algorithms across our diverse user population, addressing issues related to data quality.

Contribute to ongoing research efforts and explore new features to improve the overall performance of our products.

Help establish standards and workflows for data analysis, reporting, and dashboard development.

Participate in software development, debugging, and validation processes to ensure code and results are production-ready.

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 large-scale data and extracting valuable insights.

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.

Ability to work with time-series data; familiarity with core signal processing concepts (filters, spectral analysis/FFT, peak detection, windowing).

Domain exposure to wearable biosignals and real-world noise and artifacts.

Experience developing back-end tools for data processing and analysis.

Proficiency in C and/or Python programming languages.

Solid grounding in statistical inference (e.g., study design, hypothesis testing, regression analysis, bootstrapping), with the ability to choose appropriate methods and articulate trade-offs.

Familiarity with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, Keras, etc.

Strong communication skills with a track record of concise, visual storytelling and documentation.

Demonstrated ability to think innovatively and adapt to changing requirements while consistently producing high-quality reports within tight deadlines.

Experience with version control systems (e.g., Git), CI/CD pipelines, and cloud computing platforms (e.g., AWS).

Experience querying data in modern data warehouses (e.g., Snowflake).