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About This Role
Are you obsessed with data, partner success, taking action, and changing the game? If you have a whole lot of hustle and a touch of nerd, come work with Pattern! We want you to use your skills to push one of the fastest\-growing companies headquartered in the US to the top of the list.
Pattern accelerates brands on global ecommerce marketplaces leveraging proprietary technology and AI. Utilizing more than 66 trillion data points, sophisticated machine learning and AI models, Pattern optimizes and automates all levers of ecommerce growth for global brands, including advertising, content management, logistics and fulfillment, pricing, forecasting and customer service. Hundreds of global brands depend on Pattern’s ecommerce acceleration platform every day to drive profitable revenue growth across 60\+ global marketplaces—including Amazon, Walmart.com, Target.com, eBay, Tmall, TikTok Shop, JD, and Mercado Libre. To learn more, visit pattern.com or email \[email protected].
Pattern has been named one of the fastest growing tech companies headquartered in North America by Deloitte and one of best\-led companies by Inc. We place employee experience at the center of our business model and have been recognized as one of Newsweek’s Global Most Loved Workplaces®.
As a Junior AI Art Director, you will sit on the cutting edge of art and technology, directly shaping the visual engine driving Pattern’s ecommerce content! This is a part\-time position (20 hours per week) where you will work closely with creative, engineering, and studio teams to vet and elevate AI\-generated creative outputs spanning photography, copy, and video for hundreds of global brands. If you are interested in data driven marketing, systems design, image generation, and pushing the limits of automated content generation, this is your opportunity to disrupt the industry!
### What is a day in the life of a Junior AI Art Director?
- Vetting Creative Outputs: Reviewing and auditing automated AI image generations, copy, and video content for visual quality and brand alignment.
- Testing and Prompting: Experimenting with various image generation models and tools (such as Nanobanana ChatGpt Image, Qwen, Weavey, and local generative workflows) to test content creation across diverse product categories.
- Building Auditing Rules: Spot\-checking a wide range of generated imagery against quality benchmarks to ensure that data source assets perform in model training and generation environments.
- Studio \& Portal Collaboration: Working on site with the studio team to understand capture setups and ensuring studio photography is tuned to produce high\-fidelity content
- Feedback Loops: Communicating structured, actionable aesthetic feedback to engineering and technical teams to tune and improve the generative engine.
### What will I need to thrive in this role?
- Aesthetic Sensibility: A strong sense of taste and visual judgement (from a background in graphic design, photography, marketing, or fine arts) to evaluate brand compliance and imagery quality.
- Curiosity for Technology: An eager, open\-minded approach toward AI and generative image tools including technical systems for creative production and node\-based workflows.
- Interdisciplinary Communication: Ability to translate abstract visual and brand concepts into precise feedback that organizations can action.
- Adaptability \& Drive: High energy and comfort with rapidly evolving systems, changing parameters, and pioneering uncharted creative territory.
- In\-Person Availability: Ability to work on\-site in the office (ideally Mondays, Wednesdays, and/or Thursdays) with periodic visits to the studio.
### What does high performance look like?
- Execution \& Output Quality: Consistently identifying aesthetic defects, fine\-tuning model inputs, and maintaining high output standards in collaboration with senior leadership.
- System Integration: Successfully building and documenting auditing rules that directly improve the automated generation pipeline.
- Team \& Cross\-Functional Impact: Functioning cross\-functionally as a bridge between creative, studio operations, and engineering.
- Proactive Problem Solving: Taking initiative to test new generative models and content generation workflows as well as testing R\&D tools ahead of full rollouts.
### What is my potential for career growth?
- Pioneer Experience: Gain rare, cutting\-edge experience at the intersection of AI generation and commercial art direction that is nearly impossible to find elsewhere.
- Mentorship: Work directly under senior creative leadership to hone your visual judgment, prompt engineering, and model\-tuning skill set.
- Role Expansion: Opportunity to transition from a part\-time initial structure (20 hours/week) into a full\-time role as content production scales up.
- Cross\-Functional Exposure: Build direct working relationships with engineering, studio management, and cross\-departmental teams across Pattern.
### What does success look like in the first 30, 60, 90 days?
- First 30 Days: Develop complete familiarity with Pattern’s internal content generation processes, branding guidelines, and cross\-functional team workflows.
- First 60 Days: Begin independently vetting content output from the engine and reliably identifying the visual feedback standards expected by leadership.
- First 90 Days: Seamlessly communicate precise feedback directly to engineering teams and run point on spot\-checking portal data capture systems.
### What is the team like?
This role reports directly to the Director of AI Content. You will be joining a growing team of creative and technical professionals. In this role, you will collaborate closely with the Studio Head, Portal Stylists, Partner Art Directors and Pattern Labs team, as well as other departments including Engineering, Tech, and Marketing. This position is mentored by the Director of AI Content.
### Sounds great! What’s the company culture?
We are looking for individuals who are:
- Game Changers: A game changer is someone who looks at problems with an open mind and shares new ideas with team members, regularly reassesses existing plans and attaches a realistic timeline to goals, makes profitable, productive, and innovative contributions, and actively pursues improvements to Pattern’s processes and outcomes.
- Data Fanatics: A data fanatic is someone who recognizes problems and seeks to understand them through data, draws unbiased conclusions based on data that lead to actionable solutions, and continues to track the effects of the solutions using data.
- Partner Obsessed: An individual who is partner obsessed clearly explains the status of projects to partners and relies on constructive feedback, actively listens to partner’s expectations, and delivers results that exceed them, prioritizes the needs of your partners, and takes the time to create a personable experience for those interacting with Pattern.
- Team of Doers: Someone who is a part of a team of doers uplifts team members and recognizes their specific contributions, takes initiative to help in any circumstance, actively contributes to supporting improvements, and holds themselves accountable to the team as well as to partners.
### What is the hiring process?
- Video interview with Talent Acquisition
- Video Interview with Hiring Manager
- On\-Site Interview
- Video Interview with Department Leader
- Professional reference checks
- Executive review
- Offer
### How can I stand out as an applicant?
Strong "Nice\-to\-Haves":
- A strong visual portfolio showcasing fine art, photography, graphic design, or commercial advertising work.
- Prior exposure to node\-based workflow tools (such as Weave/Figma Weave) or basic prompting experience with generative tools (ChatGPT, Gemini, Midjourney).
- A background or educational training in Graphic Design, Photography, or Commercial Marketing.
Tips to Stand Out During the Interview:
- Showcase your portfolio: Be prepared to walk through visual examples that demonstrate your eye for detail, composition, and brand consistency. Demonstrated system design is a plus.
- Highlight your AI curiosity: Share concrete examples of how you’ve experimented with or applied AI tools in your personal, academic, or professional projects.
- Demonstrate interdisciplinary thinking: Highlight your ability to discuss technical concepts comfortably without losing sight of aesthetic quality and user experience.
Why should I work at Pattern?
Pattern offers big opportunities to make a difference in the ecommerce industry! We are a company full of talented people that evolves quickly and often. We set big goals, work tirelessly to achieve them, and we love our Pattern community. We also believe in having fun and balancing our lives, so we offer awesome benefits that include:
- Accrued PTO
- Paid Holidays
- Onsite Fitness Center
- Company Paid Life Insurance
- Casual Dress Code
- Competitive Pay
- Health, Vision, and Dental Insurance
- 401(k) match. Pattern matches 100% of the first 3% in eligible compensation deferred and 50% of the next 2% in eligible compensation deferred.
Pattern provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability, status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Pattern, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Pattern AI Hiring
Pattern has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lehi, UT, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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