Principal Machine Learning Engineer
At a glance
Senior Machine Learning Engineering role at iFIT. Remote · $185,000–$205,000 base.
remoteroles summary, based on the employer's posting.
What you'll do
- Set technical direction for personalized recommendations and search across the product, then choose experiments and guide decisions
- Build ranking and personalization systems from data pipelines and features through training, evaluation, and production delivery
- Test ranking changes, interpret results, and refine models as content, user behavior, and product surfaces change
- Work with Product and Engineering to connect model behavior to user outcomes and keep production systems reliable
- Shape privacy-conscious ML architecture with Data and Security, covering data access, privacy limits, and operational needs
What you bring
- You have built recommendation or search systems that run in production at scale
- You bring strong ML engineering skills across ranking, retrieval, feature work, model evaluation, and deployment
- You have linked model changes to measurable product or user outcomes through structured experiments
- You can build dependable pipelines and services, maintain code, and debug complex distributed systems
Who this fits
This principal-level role suits a senior ML engineer ready to set direction for ranking and personalization, mentor engineers, and contribute to hiring. The role is remote within the US, with hiring considered in listed states.
From the employer
iFIT’s vision is to create the world's most holistic health and fitness platform, integrating all elements of health - physical fitness, mental health, nutrition and active recovery - into a seamless interactive experience. We develop proprietary software that learns and adjusts to the habits of each person as it delivers immersive content that guides them on their own individual fitness journey.
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, and partner with Product and Engineering to translate model behavior into meaningful product outcomes that keep our members engaged and progressing on their fitness journeys.
COMPENSATION
$185,000 - $205,000
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.
EDUCATION & EXPERIENCE
Education and Basic 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.
Preferred Qualifications
- Experience in health, fitness, or consumer-facing recommendation platforms.
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.
Pay Range: $185,000—$205,000 USD
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.