---
title: 'Staff Applied Machine Learning Scientist at Whoop'
canonical: 'https://feeny.ai/job/staff-applied-machine-learning-scientist-whoop-boston-16rm5xjbbapc'
type: 'job'
last_seen: '2026-10-14'
---

# Staff Applied Machine Learning Scientist at Whoop

- **Company:** [Whoop](https://feeny.ai/companies/whoop)
- **Location:** Boston, MA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-06
- **Last confirmed live:** 2026-10-14
- **Apply:** https://jobs.ashbyhq.com/whoop/23264cb1-d3cf-43e6-b05a-ec5f0fca56c6

## Job description

At WHOOP, we’re on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable technology collects rich physiological data, providing members with actionable insights into their recovery, training, and sleep. We are seeking a Staff Applied Machine Learning Scientist to develop and continuously improve production-ready edge algorithms that transform sensor data into accurate, reliable, and real-time physiological insights. In this role, you will develop innovative approaches that combine deep learning, machine learning, and signal processing to deliver meaningful value to WHOOP members while optimizing for accuracy, latency, power, and scalability across wearable platforms. You will work closely with a cross-functional team of scientists, engineers, physiologists, and hardware experts to solve challenging problems at the intersection of wearable sensing, physiological modeling, and applied AI. You will develop novel modeling approaches, and own complex algorithmic problems from early development through production deployment and continuous improvement. Your work will directly shape the future of WHOOP’s sensing capabilities and our ability to provide members with accurate, personalized, and actionable insights into their health and performance.

## RESPONSIBILITIES:

- Design advanced algorithms that combine signal processing, physiological modeling, machine learning, and deep learning for physiological time-series and multimodal sensor data, with a focus on accuracy, robustness, and generalization across diverse members and real-world conditions.
- Own the development and continuous improvement of production-ready edge algorithms that transform multimodal sensor data into accurate, reliable, and real-time physiological insights.
- Analyze large-scale wearable sensor datasets to identify performance gaps, characterize challenging conditions, and drive data-informed algorithm improvements.
- Define rigorous evaluation methodologies, validation frameworks, and performance metrics to assess algorithms throughout development and deployment.
- Optimize algorithms for embedded deployment, balancing accuracy with power, memory, latency, and compute constraints across current and future wearable platforms.
- Set technical direction and establish best practices for modeling, experimentation, validation, and algorithm development across complex sensing problems.
- Pursue ambiguous, high-impact technical initiatives from research and prototyping through validation, production deployment, monitoring, and continuous improvement.
- Collaborate closely with Data Science, Firmware, Software, Hardware, Product, and domain experts to translate algorithmic innovations into production-ready capabilities and member-facing features.
- Stay at the forefront of advances in deep learning, machine learning, signal processing, edge AI, and physiological sensing, and translate relevant innovations into differentiated WHOOP capabilities.

## QUALIFICATIONS:

- MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, Machine Learning, Applied Mathematics, or a related quantitative field.
- 7+ years of experience developing and deploying machine learning, deep learning, and/or signal processing algorithms for complex real-world applications.
- Deep technical expertise in modern machine learning and deep learning methods, particularly for time-series and multimodal sensor data.
- Strong foundation in digital and statistical signal processing, with the ability to combine classical signal processing techniques with modern learning-based approaches.
- Strong proficiency in Python for algorithm development, experimentation, and large-scale data analysis; experience with C/C++ and embedded algorithm development is highly desirable.
- Demonstrated ability to develop robust models using large, noisy, real-world datasets and achieve strong generalization across diverse conditions.
- Experience designing rigorous experiments, defining performance metrics, performing error analysis, and translating findings into algorithm improvements.
- Experience taking algorithms through the full development lifecycle, from research and prototyping through validation, deployment, monitoring, and continuous improvement.
- Strong analytical and problem-solving skills, with the ability to navigate ambiguity and make sound technical decisions.
- Experience with physiological signals, wearable sensors, or biomedical sensing is highly desirable.
- Experience optimizing algorithms for resource-constrained embedded or edge environments, including tradeoffs across accuracy, power, memory, compute, and latency, is a plus.
- Strong commitment to embracing and leveraging AI tools in day-to-day work while maintaining high standards for quality and rigor.

Join us in pushing the boundaries of wearable sensing, applied AI, and physiological intelligence and positively impacting people’s lives! This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office. Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply. WHOOP is an Equal Opportunity Employer and participates in E-Verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

## About Whoop

## Company Overview
- **One-liner**: WHOOP is a human performance optimization platform that uses a wearable device and 24/7 monitoring to help members improve sleep, strain, recovery, and overall health.
- **Entity Type**: Private (funding stage not publicly available in provided sources)
- **Headquarters**: Boston, Massachusetts, USA
- **Founded**: 2012
- **Founders**: Will Ahmed

## Core Business
- **Primary industry**: Wearable health technology, human performance optimization
- **Target customers**: B2C (consumers, athletes) and B2B (professional sports teams, organizations)
- **Mission**: “To unlock human performance and Healthspan.” [whoop.com - About](https://www.whoop.com/us/en/about/)

## Products & Services
- **WHOOP 5.0 + Subscription**: A non-screen wrist-worn wearable with 14+ day battery life that continuously monitors sleep, strain, recovery, heart rate, heart rate variability, and 65+ biomarkers. The platform provides personalized coaching, blood pressure insights, women’s hormonal insights, VO₂ max, heart rate zones, and mental health tracking. Type: Hardware + SaaS subscription. [whoop.com - Homepage](https://www.whoop.com/us/en/)

## Market Standing
- **Valuation/Market Cap**: Not publicly available from provided sources
- **Key Metric**: Not publicly available (annual revenue not disclosed); estimated headcount of 500 employees [Built In Boston](https://www.builtinboston.com/company/whoop)
- **Notable Investors/Partners**: Not disclosed in provided sources
- **Growth Signals**: 500 total employees; hybrid workspace (4 days on-site per week); actively hiring across Engineering, AI/ML, Data & Analytics, Product, and Design; only 0.13% of applicants are hired, indicating high selectivity and strong talent demand. [whoop.com - Careers](https://www.whoop.com/us/en/careers/) | [Built In Boston](https://www.builtinboston.com/company/whoop)

## Competitive Advantages
- Proprietary 24/7 physiological monitoring with advanced sensor technology and an ultra-long battery life (14+ days)
- Deep scientific foundation – founded after reviewing over 500 medical papers
- Personalized coaching and insight delivery based on individual biometric data
- Strong brand recognition among elite athletes and mainstream consumers
- Privacy-first design with encrypted data storage [whoop.com - Homepage](https://www.whoop.com/us/en/)

## Strategic Focus
- Expanding health insights: introducing blood pressure monitoring, women’s hormonal health features, and heart health tracking
- Enhancing AI/ML capabilities (evident from many open AI/ML roles) [jobs.lever.co](https://jobs.lever.co/whoop)
- Deepening enterprise/B2B partnerships with sports organizations and research institutions
- Continuing to build out Healthspan and longevity features

## Why Work Here
- **Hybrid workspace** – employees are on-site 4 days a week in Boston’s “best office” [whoop.com - Careers](https://www.whoop.com/us/en/careers/)
- **Benefits** – flexible time off, above-market compensation, paid parental leave, health support [whoop.com - Careers](https://www.whoop.com/us/en/careers/)
- **Employee Resource Groups** – ADAPT (disability inclusion), BLACK@WHOOP, LGBTQ+ (WHOOP GOOP), Veterans and Military Personnel, Women of WHOOP, Women in Science and Engineering, WHOOP Parents [whoop.com - Careers](https://www.whoop.com/us/en/careers/)
- **High selectivity** – only 0.13% of applicants are hired, reflecting a rigorous, standards-driven culture [whoop.com - Careers](https://www.whoop.com/us/en/careers/)
- **Equal Opportunity Employer** – committed to diversity and inclusion in all employment decisions [whoop.com - Careers](https://www.whoop.com/us/en/careers/)

## Sources
1. [whoop.com - Careers](https://www.whoop.com/us/en/careers/)
2. [whoop.com - About](https://www.whoop.com/us/en/about/)
3. [whoop.com - Homepage](https://www.whoop.com/us/en/)
4. [Built In Boston - WHOOP Office Profile](https://www.builtinboston.com/company/whoop)
5. [jobs.lever.co - WHOOP Job Listings](https://jobs.lever.co/whoop)

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