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 Staff Machine Learning Engineer 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 cross-functionally with data scientists, MLOps engineers, and software developers to design, train, deploy, and maintain machine learning algorithms that power training features. You'll also partner with product managers to identify opportunities for delivering novel, data-driven insights to WHOOP members.
RESPONSIBILITIES:Design, train, and optimize machine learning algorithms for movement, exercise and training applications across diverse backend platformsCollaborate closely with data scientists, ML Ops and software engineering teams to ensure reliable deployment, observability, and robust integration with the WHOOP ecosystemContribute to technical roadmap development and architectural decision-making and strategy across the data science and research departmentCollaborate with product managers to design impactful, member-facing featuresWork closely with a team of data scientists in developing algorithms that power member-facing featuresWork with Data Engineers to improve data pipelining, tooling for machine learning, and systems for quality and validation Mentor other data scientists on the team, by providing actionable feedback for more junior data scientists on technical areas of growth Periodically serve as the on-call data scientist to respond in real time to incidents affecting production servicesQUALIFICATIONS:Bachelor's Degree in Mathematics, Statistics, Computer Science, or a related field7+ years of ML engineering, applied research, or a similar role4+ years experience applying advanced mathematical and statistical techniquesExperience 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 toolsExperience working with time series data, preferably with wearable data applicationsProficiency in scientific Python and SQLExcellent verbal and written communication skillsThis 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.
The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values.
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 $170,000-$230,000. 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.
