Senior Machine Learning Engineer, Core Algorithms

2 months ago

Boston, MA, USASenior

H1B sponsor

Base Salary

$150k - $210k/yr

Responsibilities

Create, improve, and maintain production services for core health and fitness features with applied ML scientists and MLOps engineers.
Improve ML data pipelines, tooling, validation systems, and operational infrastructure for robust model performance.
Translate research prototypes into scalable production ML systems optimized for latency and cost.
Collaborate with researchers and Product teams to align model development with physiological insights and member impact.
Develop observability, alerting, monitoring, performance validation, and incident-response processes, including on-call rotations.
Mentor engineers and applied ML scientists and contribute to code and system design reviews.

Bachelor’s degree in Computer Science, Data Science, Applied Mathematics, or a related field; a master’s degree is preferred.
At least 4 years of professional experience as an ML engineer, applied researcher, or software engineer focused on ML systems.
Experience working with time-series data, including wearable, physiological, or high-frequency sensor data.
Strong Python coding skills and experience writing clean, production-quality code.
Experience designing, deploying, and operating production-scale ML systems for millions of users, real-time streaming, or large-scale batch workloads.
Experience deploying and maintaining ML backend services with APIs, reliability, observability, and monitoring using AWS or GCP.
Preferred: at least 2 years applying advanced mathematical and statistical techniques.

Full-time position with a U.S. base salary range of $150,000-$210,000, plus equity and benefits.
Equity package aligned with the company’s long-term growth.
Role is based in WHOOP’s Boston, Massachusetts office, and the successful candidate must relocate if necessary to work from the office.
WHOOP is an equal opportunity employer and participates in E-Verify.

501-1,000 employees

Solving problems that sit at a unique intersection of hardware, tech, health, fitness and design.