Principal Machine Learning Engineer

About the role

iFIT’s vision is to create the world's most holistic health and fitness platform, integrating all elements of health - physical fitness, mental health, nutrition and active recovery - into a seamless interactive experience. We develop proprietary software that learns and adjusts to the habits of each person as it delivers immersive content that guides them on their own individual fitness journey.

We are currently seeking an ambitious pace-setter to join our team as a Principal Machine Learning Engineer remotely in the US.

iFIT is looking for a technically exceptional and strategic Principal Machine Learning Engineer to own the recommendation and search personalization strategy across our product surfaces. In this role, you will design and build end-to-end ML systems, drive relevance and ranking improvements through rigorous experimentation, and partner with Product and Engineering to translate model behavior into meaningful product outcomes that keep our members engaged and progressing on their fitness journeys.

COMPENSATION $185,000 - $205,000

ROLE COMMITMENTS

- Own and drive the recommendation and search personalization strategy across all product surfaces

- Build end-to-end ML systems — data pipelines, feature engineering, model training, and production serving — that scale with the platform

- Drive relevance improvements through rigorous experimentation, ensuring model gains translate to measurable user outcomes

- Build a scalable, privacy-first ML architecture that addresses data access, compliance, and security requirements without slowing delivery

- Raise ML fluency across R&D — mentor engineers, contribute to hiring, and serve as the internal authority on ranking and personalization

ESSENTIAL DUTIES AND RESPONSIBILITIES

- Own the 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 ML systems for ranking, retrieval, and personalization, including data pipelines, feature engineering, model training and evaluation, and production serving infrastructure.

- Drive relevance and ranking improvements through rigorous evaluation and experimentation — design experiments, interpret results, and iterate quickly 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 ensure safe, scalable personalization architecture that addresses data access, privacy constraints, and operational requirements as the platform grows.

EDUCATION & EXPERIENCE Education and Basic Qualifications

- Demonstrated experience building recommendation and/or search systems that operate in production at scale.