Principal Machine Learning Engineer

iFIT

16h • $185,000 - $205,000 • Remote

About The Position

iFIT is seeking a technically exceptional and strategic Principal Machine Learning Engineer to own recommendation and search personalization across product surfaces. The role involves designing and building end-to-end machine learning systems, improving relevance and ranking through experimentation, and partnering with Product and Engineering to turn model behavior into product outcomes that support member engagement and progress. The position is remote in the United States.

The engineer will shape the technical strategy for recommendation and search, build scalable systems spanning data pipelines, feature engineering, model training and evaluation, and production serving, and develop privacy-first architecture that meets data access, compliance, security, and operational requirements. The role also includes mentoring engineers, contributing to hiring, and serving as an internal authority on ranking and personalization.

Requirements

• Demonstrated experience building recommendation and/or search systems operating in production at scale.
• Strong machine learning engineering fundamentals, including ranking and retrieval, feature engineering, model training and evaluation, and practical deployment considerations.
• Proven ability to connect model improvements to measurable user and product outcomes through structured experimentation.
• Strong software engineering skills, including building reliable pipelines and services, writing maintainable code, and debugging complex distributed systems.
• Ability to communicate clearly with Product and Engineering partners, explain trade-offs, and align cross-functional teams on direction.
• Authorized to work in the United States without sponsorship.

Nice To Haves

• Experience in health, fitness, or consumer-facing recommendation platforms.

Responsibilities

• Own recommendation and search personalization strategy across product surfaces; define the technical approach, prioritize experiments, and drive decisions from offline evaluation to real-world user impact.
• Design and build end-to-end machine learning systems for ranking, retrieval, and personalization, including data pipelines, feature engineering, model training and evaluation, and production serving infrastructure.
• Improve relevance and ranking through rigorous evaluation and experimentation; design experiments, interpret results, and iterate in response to new content, user behavior, and product surfaces.
• Partner with Product and Engineering to translate model behavior into product outcomes, collaborate on integration, and ensure systems perform reliably in production.
• Collaborate with Data and Security to build safe, scalable personalization architecture that addresses data access, privacy constraints, and operational requirements.
• Mentor engineers, contribute to hiring, and raise machine learning fluency across R&D.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed