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Principal Machine Learning Engineer

🕒 August 6

🏈 Alabama, Alaska, +35 more states – Remote

infoinfo

💵 $185k - $205k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

Where this job comes from

Posted on iFIT's careers page, not through a job board

First seen by Remote Rocketship August 6

Still open on iFIT's site. Checked 3 hours ago.

Logo of iFIT

iFIT

1001 - 5000 employees

Founded 1977

🧘 Wellness

☁️ SaaS

🔧 Hardware

💰 $200M Private Equity Round - iFit on 2019-12

Wellness • SaaS • Hardware

iFIT is a connected fitness company that provides interactive workouts, personal training content, and a subscription-based fitness platform integrated with exercise equipment. iFIT publishes on-demand and live classes (Global Workouts, Progressive Series), offers mobile apps for iOS and Android, and supports equipment brands including NordicTrack and ProForm. The company operates a member center, customer support (live chat and texting), order tracking, and resources for memberships, troubleshooting, assembly and repair.

📋 Description

• Own and drive the recommendation and search personalization strategy across all product surfaces. • Build end-to-end ML systems, including data pipelines, feature engineering, model training, evaluation, and production serving infrastructure. • Drive relevance and ranking improvements through rigorous evaluation and experimentation. • Partner with Product and Engineering to translate model behavior into product outcomes and ensure reliable production integration. • Collaborate with Data and Security on safe, scalable personalization architecture addressing data access, privacy constraints, and operational requirements. • Mentor engineers, contribute to hiring, and serve as the internal authority on ranking and personalization.

🎯 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. • Authorized to work in the United States without sponsorship. • Preferred: Experience in health, fitness, or consumer-facing recommendation platforms.

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