
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.
🕒 August 6
🏈 Alabama, Alaska, +35 more states – Remote
💵 $185k - $205k / year
⏰ Full Time
🔴 Lead
🤖 Machine Learning Engineer
👻 Ghost score 27%
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.

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.
• 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.
• 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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