Our Machine Learning Core Algorithms team is responsible for developing novel algorithms and features that expand our health and fitness capabilities with wearable sensor data. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members. This role is located on our Core Algorithms team: performance-related insights for sleep, recovery, and exercise.
As a Senior Machine Learning Engineer on our Core Algorithms team, you will design, build, and productionize ML systems that deliver meaningful, personalized metrics to millions of members. You will own the ML systems you build in collaboration with applied machine learning scientists. You will work at the intersection of data science, backend engineering, and cloud infrastructure - deploying robust, scalable, and reliable ML solutions built on physiological and behavioral data streams. This role emphasizes strong coding skills, system design, and the ability to deliver production-ready ML systems.
Responsibilities:- Create, improve, and maintain production services that provide analysis for core features in collaboration with applied ML scientists and MLOps engineers.
- Improve ML data pipelines, tooling, and validation systems that support robust model performance.
- Work alongside applied ML scientists to translate research prototypes into production ML systems optimized for scale, latency and cost efficiency.
- Collaborate with researchers and Product teams to align model development with physiological insights and member impact.
- Build operational maturity within the Core Algorithms team as well as the broader Machine Learning team, developing processes for observability, alerting, and incident response (including on-call rotations).
- Develop applied ML operational infrastructure (frameworks, evaluation criteria, performance validation).
- Mentor other engineers and applied ML scientists on production practices, raising the bar through code and system design review.
Qualifications:- Bachelor's Degree in Computer Science, Data Science, Applied Mathematics, or a related field (Master's preferred).
- 4+ years of professional experience as an ML engineer, applied researcher, or software engineer with a focus on ML systems.
- Experience working with time series data (wearable, physiological, or high-frequency sensor data).
- Strong coding skills in Python with a track record of writing clean, production-quality code.
- Experience designing, deploying and operating ML production systems at production scale (millions of users, real-time streaming and/or large-scale batch).
- Experience deploying and maintaining ML backend services (APIs, reliability, observability, monitoring; AWS or GCP).
- Preferred: 2+ years experience applying advanced mathematical and statistical techniques.
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.
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 $150,000-$210,000. 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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