Machine Learning Engineer II – Health Insights
| Company | Whoop |
|---|---|
| Location | Boston, MA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s |
| Experience Level | Mid Level |
Requirements
- 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.
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.
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.