Machine Learning Engineer II – Training

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. 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.

RESPONSIBILITIES

• 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

QUALIFICATIONS

• 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.

• Preferred experience working with time series data, preferably with wearable data applications.

• Proficiency in scientific Python and SQL.

• Excellent verbal and written communication skills.

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 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.

At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long‑term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long‑term growth and success.

The U.S. base salary range for this full‑time position is $125,000‑$170,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job‑related skills, experience, performance, and relevant education or training.

In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.

These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.

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