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As a Machine Learning Engineer II on our Training team, you will develop algorithmic features and metrics that capture aspects of daily movement, exercise, and training. You'll integrate diverse data sources—including WHOOP sensor data and gold-standard reference datasets—while grounding your work in clinical theory and scientific literature. You’ll work with data from a variety of sources including processed time series data generated by sensors on the WHOOP Strap, data collected from “gold-standard” devices, and data entered manually by WHOOP members via the mobile application. Using these data sources, as well as drawing upon clinical theory and evidence, you will design, train, and deploy machine learning algorithms to analyze training data. We’re looking for someone who has experience developing ML models with large datasets in Python and who is excited about the use of wearables in the health and wellness space.
Job Responsibility:
Design, train, and optimize machine learning algorithms for movement, exercise and training applications across diverse backend platforms
Collaborate closely with data scientists, ML Ops and software engineering teams to ensure reliable deployment, observability, and robust integration with the WHOOP ecosystem
Contribute to technical roadmap development and architectural decision-making for projects that you are involved in
Work closely with a team of data scientists in developing algorithms that power member-facing features
Work with Data Engineers to improve data pipelining, tooling for machine learning, and systems for quality and validation
Periodically serve as the on-call data scientist to respond in real time to incidents affecting production services
Requirements:
Bachelor's Degree in Mathematics, Statistics, Computer Science, or a related field
2+ years of ML engineering, applied research, or a similar role
2+ years experience applying advanced mathematical and statistical techniques
Experience deploying and maintaining production ML systems on cloud platforms (e.g., Kubernetes, AWS, GCP)
Familiarity with MLOps best practices and the ability to collaborate effectively with infrastructure teams on Docker, CI/CD workflows, model versioning, and observability tools
Proficiency in scientific Python and SQL
Excellent verbal and written communication skills
Nice to have:
Preferred experience working with time series data, preferably with wearable data applications
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