Machine Learning Engineer II

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.