Junior AI Art Director

About the role

This role evaluates and elevates AI-generated creative for global ecommerce brands, coordinating with creative, studio, and engineering teams. It operates part-time (20 hours per week) and reports to the Director of AI Content.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

The team assesses visual quality and brand alignment of AI-generated photography, copy, and video for ecommerce campaigns.

Diverse product categories undergo evaluation through varied image generation models and experimental prompting approaches.

Benchmark comparisons validate data source performance across training and production environments for generated imagery.

The studio team refines photography workflows by analyzing capture setups to tune source fidelity.

Built and documented auditing rules enhance automated content generation pipelines.

Cross-functional coordination between creative, studio operations, and engineering enables consistent execution.

Output defects are detected and model inputs are fine-tuned alongside senior leadership to maintain high aesthetic standards.

Quality reviews oversee portal data capture systems and support system integration efforts.

Requirements

The posting states a bachelor's degree requirement. Strong visual judgment and taste are demonstrated, with background in graphic design, photography, marketing, or fine arts.

Curiosity toward AI and generative image tools is shown, including experience with node-based workflows and creative production systems.

Abstract visual and brand concepts are translated into precise, actionable feedback for organizations.

Rapidly evolving systems and pioneering creative environments are navigated with high energy and adaptability.

On-site availability in Lehi is required, including scheduled studio visits and alignment on core days when possible.

Practical notes

This role is part-time (20 hours per week) and may evolve into full-time as content production scales. The position is based in Lehi, with a requirement for in-office availability and periodic studio visits.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

AI-driven ecommerce optimization combines creative direction with data and systems design. The role uses generative image, copy, and video tools within production workflows. Collaboration across creative, engineering, and studio teams defines day-to-day impact. Continuous experimentation and structured feedback drive improvements in automated content generation. The position offers mentorship from senior creative leadership and exposure to commercial AI applications.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.