Marketing Engineer — AI Enablement & Automation

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 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 across channels are gated on measured lift, not platform-reported numbers.

Raise the dashboarding bar by shipping a standardized, exec-quality dashboard system with templates, brand styling, and automated refresh so every team deliverable is self-serve, current, and something leadership actually opens.

ESSENTIAL DUTIES AND RESPONSIBILITIES

Own the team's Claude Code setup end-to-end including project conventions, reusable skills, custom agents, MCP servers, and permission allowlists.

Convert one-off analyst workflows into shared, documented, versioned skills with dry-run defaults and confirm gates on anything that writes to a live ad account.

Run onboarding and office hours, pair with analysts to level up prompting, tool-building, and review habits, and be the go-to person for anyone stuck on an AI workflow.

Evaluate and integrate new capability safely including new MCP connectors, scheduled agents, and workflow orchestration.

Automate recurring pipelines including reporting refreshes, warehouse cache loaders, data-quality checks, and spend-delivery QA on live geo tests.

Build and maintain API integrations across the ad stack including Meta, Google Ads, Reddit, Impact.com, Search Console, and MMP postbacks — including token rotation and expiry monitoring, rate limits, and schema-drift alerts.

Own campaign-ops automation with full write authority including geo holdout apply and teardown with rollback snapshots, budget changes normalized against original baselines, and read-modify-write discipline on targeting objects.

Own credential hygiene through AWS Secrets Manager with expiry alerting so a dead token never silently kills a pipeline.

Build and maintain stakeholder-facing reporting including exec-quality dashboards and readouts for weekly performance, plan-vs-actual, incrementality results, and media-mix outputs.

Support measurement work including geo experiments, MMM, and LTV/CAC on the engineering side — data plumbing, refresh automation, QA, and readout generation — while analysts own the methodology.

Standardize dashboard patterns and brand styling so every team deliverable looks like it came from one team.

Define and encode what "safe" means for agentic writes to ad platforms through dry-run defaults, confirmation gates, audit logs, and rollback paths.

Manage internal app deployments for self-serve reporting tools.

Track usage, cost, and adoption of AI tooling across the team.