Our data science algorithms teams are responsible for developing novel algorithms and features that expand our health and fitness capabilities with medical-grade metrics. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members. Currently, we have two Senior MLE roles open across two teams: DS Health team: novel algorithms within the domains of women's health, multimodal longitudinal health insights, and SaMD DS Core Algos team: performance-related insights for sleep, recovery, or exercise As a Senior Machine Learning Engineer on our Core Algos or Health team, you will design, build, and productionize ML systems that deliver meaningful, personalized health metrics to millions of members. You will work at the intersection of data science, backend engineer, and cloud infrastructure - deploying robust, scalable, and reliable ML solutions build on physiological and behavioral data streams. This role emphasizes strong coding skills, system design, and ability to deliver production-ready ML systems. RESPONSIBILITIES: - Create, improve, and maintain production services that provide analysis for health features in collaboration with data scientists and MLOps engineers - Collaborate with data engineers to improve ML data pipelines, tooling, and validation systems that support robust model performance - Work alongside data 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 - Participate in on-call rotations for data science services, ensuring uptime and performance in production environments QUALIFICATIONS: - Bachelor's Degree in Computer Science, Data Science, Applied Mathematics, or a related field (Master's preferred). - 4+ years of professional experience as a ML engineer, applied researcher, or software engineer with a focus on ML systems - Strong coding skills in Python with a track record of writing clean, production-quality code - Experience designing, deploying and operating ML inference systems at scale (real-time streaming and/or large-scale batch) - Strong fundamentals in backend/service development (APIs, reliability, monitoring, debugging) as it relates to serving ML models - Experience deploying and maintaining ML systems on cloud platforms (AWS or GCP), including CI/CD and observability practices - Familiarity with applied ML development (frameworks, evaluation criteria, performance validation) and translating prototypes into production systems - Preferred: 2+ years of experience applying advanced mathematical and statistical techniques - Preferred: Experience working with time series data (wearable, physiological, or high-frequency sensor data) 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.