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As a Machine Learning Engineer II, you'll architect ML systems, optimize models, and deploy solutions while collaborating with engineering teams to enhance health metrics.
WHOOP is an advanced health and fitness wearable, on a mission to unlock human performance. WHOOP empowers its members to improve their health and perform at a higher level by providing a deep understanding of their bodies and daily lives.
The Health Insights team is responsible for developing novel algorithms and features that expand our health capabilities. Our work spans several key areas, including women’s health, medical device-grade metrics, wellness monitoring, longevity research, and emerging health insights. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members.
As a Machine Learning Engineer II on our Health Insights team, you will help develop and deploy machine learning systems that deliver meaningful, personalized health metrics to millions of members. You will work at the intersection of data science, backend engineering, and health research, contributing to scalable ML solutions built on physiological and behavioral data streams. This role emphasizes robust system design, performance, and reliability in production.
RESPONSIBILITIES:
- Architect and optimize ML systems and models to ensure efficiency and scalability in production environments.
- Design, implement, and maintain scalable machine learning inference pipelines that power core health features.
- Deploy models and build robust backend services that integrate seamlessly with the WHOOP platform.
- Collaborate closely with MLOps and software engineering teams to ensure reliable deployment, monitoring, and infrastructure support for model serving.
- Establish and uphold performance, observability, and accuracy standards through rigorous testing, validation, and continuous evaluation.
QUALIFICATIONS:
- Bachelor’s degree in Computer Science, Machine Learning, Applied Mathematics, Statistics, or a related field.
- 2+ years of professional experience delivering ML-driven solutions in production environments.
- Proficient in Python with experience using ML libraries (e.g., scikit-learn, PyTorch, TensorFlow) and numerical packages (e.g., numpy, scipy, pandas).
- Knowledge of software development best practices including Git, testing, CI/CD, and Docker.
- Experience operating ML services in production, including monitoring, alerting, and troubleshooting.
- Exposure to tools and platforms for ML infrastructure (e.g., Airflow, MLflow, AWS/GCP, Kubernetes).
- Excellent communication skills with the ability to collaborate effectively across technical and non-technical teams.
PREFERRED QUALIFICATIONS:
- Understanding of digital health and physiological monitoring concepts.
- Knowledge of modern MLOps practices and the full lifecycle of ML systems.
- Experience with statistical analysis and causal inference methods for deriving actionable insights from observational data.
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.
Top Skills
Airflow
AWS
Docker
GCP
Git
Kubernetes
Mlflow
Numpy
Pandas
Python
PyTorch
Scikit-Learn
Scipy
TensorFlow
WHOOP Boston, Massachusetts, USA Office
1 Kenmore Sq, Boston, MA, United States, 02215
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