Marketing Engineer — AI Enablement & Automation

About iFIT

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.

About the Role

We are currently seeking an ambitious pace-setter to join our team as a Marketing Engineer - AI Enablement & Automation, remotely in the US or hybrid in Logan, Utah.

iFIT is an AI-enabled organization — AI-assisted work is the default here, not an experiment — and this role has access to enterprise Claude and the full enterprise toolchain from day one. Our marketing team already runs on AI-assisted engineering: Claude Code drives reporting pipelines, incrementality testing, media-mix modeling, and campaign operations against a production stack including Snowflake, GA4, SAP, ad-platform APIs, and AWS. We are hiring the person who turns that momentum into a system. This is first an enablement-and-automation role — you will own how the team uses AI tooling: conventions, guardrails, shared skills and agents, credential hygiene, and the automation that removes humans from loops they don't need to be in. This role carries write authority over live ad accounts — your automations will change budgets, targeting, and campaign state on real spend, end to end.

Role Commitments

• Automate core processes by taking recurring workflows — reporting refreshes, data loads, spend QA, and campaign operations — from manual to hands-off, with guardrails and rollback paths that make unattended automation safe on live spend.
• Give the team its hours back by measurably reducing analyst toil, with hours saved tracked and reported as a first-class KPI of this role.
• Advance our media-mix model by hardening it into a versioned, validated product with out-of-sample validation as standard practice, refresh automation, prior and sensitivity checks, and calibration of response curves against experiment readouts.
• Advance testing and channel allocation by industrializing incrementality testing — geo-experiment tooling, always-on test pipelines, and clean readouts — so budget-allocation decisions