Principal Machine Learning Engineer

Role Description

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.

• Design and build end-to-end ML systems.
• Drive relevance and ranking improvements through rigorous experimentation.
• Partner with Product and Engineering to translate model behavior into meaningful product outcomes.

Qualifications

• Demonstrated experience building recommendation and/or search systems that operate in production at scale.
• Strong ML engineering fundamentals including ranking and retrieval concepts, 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.

Requirements

• 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.
• Collaborate with Data and Security to ensure safe, scalable personalization architecture that addresses data access, privacy constraints, and operational requirements as the platform grows.

Benefits

• Compensation: $185,000 - $205,000