Principal Machine Learning Engineer

iFIT is seeking a Principal Machine Learning Engineer to own recommendation and search personalization across its health and fitness platform. The role involves building and improving end-to-end ML systems, driving experimentation and relevance, and partnering across Product, Engineering, Data, and Security to deliver reliable, privacy-conscious personalization.

Responsibilities:

• 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
• 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

Requirements:

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