[Hiring] Principal Machine Learning Engineer @iFIT

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. In this role, you will:

• 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.

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.

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.

Preferred Qualifications

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

Compensation

$185,000 - $205,000

Disclaimer

Compensation may vary based on the job level, your geographic work location, position incentive plan, and exemption status. Although we currently may consider hiring in the states listed below, not all positions are available in all locations, and work in a particular state is not guaranteed.

• Current list of states where iFIT may consider hiring: AK, AL, AR, AZ, CA, CO, CT, FL, GA, ID, IL, IN, KS, KY, LA, MA, MD, MI, MN, MO, MS, NC, NH, NJ, NV, NY, OH, OK, OR, PA, RI, SC, SD, TN, TX, UT, VA, WA, WI, WY.

Subject to applicable state laws, your employment at iFIT is "at-will". At-will employment means that you and the company each have the right to terminate the employment relationship at any time for any cause or for no cause at all.

iFIT does not discriminate in employment opportunities or practices on the basis of race, color, religion, sex, national origin, age, ancestry, mental or physical disability, sexual orientation, gender identity, medical condition, genetic information, marital status, veteran status or any other characteristic protected by law.