Senior Machine Learning Engineer (Data Science Algorithms)

TL;DR

Senior Machine Learning Engineer (Data Science Algorithms): Designing and productionizing ML systems for health and fitness metrics with an accent on scalability, latency, and cost-efficiency. Focus on translating research prototypes into production-ready pipelines using Python and cloud infrastructure to support physiological data insights.

Location: Must be based in or be able to relocate to the office in Boston, MA.

Salary: $150,000–$210,000.

Company

An advanced health and fitness wearable company dedicated to unlocking human performance through deep physiological insights.

What you will do

• Develop and maintain production services for health features alongside data scientists and MLOps engineers.
• Translate research prototypes into optimized, scalable ML systems.
• Improve ML data pipelines, tooling, and validation frameworks.
• Collaborate with product teams to align model development with physiological insights.
• Participate in on-call rotations to ensure system reliability and performance.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Applied Mathematics, or related field.
• 4+ years of professional experience as an ML engineer or applied researcher.
• Strong coding proficiency in Python.
• Proven track record of designing, deploying, and operating ML inference systems at scale.
• Experience with cloud platforms like AWS or GCP, including CI/CD and observability.
• Must be based in or able to relocate to Boston, MA.

Nice to have

• Master’s degree in a technical field.
• 2+ years of experience applying advanced mathematical and statistical techniques.
• Experience working with time series data, specifically wearable or high-frequency sensor streams.

Culture & Benefits

• Comprehensive benefits package including health insurance.
• Generous equity package providing ownership in company growth.
• Focus on a diverse and inclusive work environment.
• Mission-driven culture focused on human performance and medical-grade metrics.