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Job Title: Junior AI Imaging Artist
Location: New York, NY (Flatiron HQ) – 3 days onsite, 2 days remote
About The Job
TileBar is an innovative industry leader transforming the tile, interiors and surfaces market in the United States. As a rapidly growing company, we are redefining how homeowners and designers shop for projects.
TileBar is looking for a highly organized Junior AI Imaging Artist to join our creative team and support the production, management, and enhancement of visual content across ecommerce, marketing, and brand campaigns. This role is ideal for someone early in their creative career who thrives in fast-paced environments, has strong technical organization skills, and is excited by the intersection of photography and AI-powered creative tools.
In addition to retouching and AI-assisted image creation, this position plays a critical role in maintaining and managing TileBar’s growing digital asset library and ensuring seamless organization across creative workflows.
This role will report directly to the Senior Manager of Photography – Post-Production and work closely with the creative, photography, and design teams to support daily asset management, retouching workflows, and AI-assisted content production.
Essential Duties and Responsibilities
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. Other minor duties may be assigned.
Assist with high-quality photo retouching and editing for ecommerce, marketing, social media, and brand campaigns.
Utilize AI-powered creative tools and automation workflows to support image processing and creative production.
Take ownership of digital asset management processes, ensuring all imagery, creative files, and deliverables are accurately organized, tagged, archived, and easily accessible.
Maintain strict file naming conventions, folder structures, metadata standards, and version control across all creative assets.
Support the organization and upkeep of TileBar’s photography and AI-generated content libraries.
Create and enhance visual assets using generative AI platforms such as Adobe Firefly, Runway, Midjourney, Stable Diffusion, Nano Banana, or similar tools.
Handle asset distribution, file transfers, and communications with third-party retouching vendors to ensure deadlines, quality standards, and workflow expectations are met.
Assist in the creation of photo retouching decks, markup documents, and visual guides to communicate retouching direction and creative feedback clearly across teams and vendors.
Collaborate closely with photographers, designers, post-production teams, and creative stakeholders to maintain visual consistency and efficient workflows.
Prepare and optimize imagery for web, ecommerce, and digital platforms while ensuring quality control and brand alignment.
Assist with creative production prep for photo shoots and AI-assisted content creation projects.
Stay current on emerging AI trends, creative technologies, and evolving digital asset management best practices.
Qualifications
The requirements listed below are representative of the qualifications necessary to perform the job.
1–2 years of experience in photo retouching, digital content creation, digital asset management, or related creative work.
Must be highly proficient in Adobe Photoshop with strong retouching, compositing, color correction, and image editing skills.
Strong organizational skills with exceptional attention to detail and the ability to manage large volumes of digital assets efficiently.
Strong understanding of digital asset management workflows, file organization systems, version control, and archive maintenance.
Experience coordinating assets and communications with external vendors, freelancers, or post-production partners is a plus.
Familiarity with AI creative tools such as Adobe Firefly, Midjourney, Runway, Stable Diffusion, Nano Banana, or similar platforms.
Portfolio demonstrating photography, retouching, design, or AI-assisted creative work.
Ability to multitask and manage multiple projects in a fast-paced creative environment.
Strong communication and collaboration skills.
Interest in ecommerce, interiors, luxury, or design-driven brand aesthetics.
Preferred Qualifications
Experience working in an ecommerce brand, creative agency, or production studio environment.
Familiarity with DAM systems, post-production workflows, or studio asset pipelines.
Knowledge of Monday.com, Figma, and collaborative project management workflows.
Basic understanding of AI prompting, image generation workflows, or AI model customization.
Passion for emerging technologies, creative innovation, and visual storytelling.
Talent Standards for Success
At TileBar, how we work matters as much as what we achieve. In this role, success will also be measured by how consistently you demonstrate TileBar’s five Talent Standards.
Results Ownership & Accountability
Takes ownership of responsibilities and outcomes, communicates proactively, manages commitments and deadlines responsibly, follows through, and addresses risks or issues that may impact results.
Collaboration & Team Contribution
Works well with others, supports team and company goals, shares information appropriately, and builds productive relationships with colleagues, leaders, and cross-functional partners.
Organizational Mindset
Understands how individual work, decisions, and priorities affect other people, teams, customers, and the broader company.
Balances role-specific responsibilities with what supports the best overall outcome for TileBar.
Adaptability & Continuous Improvement
Adapts to changing priorities, learns new ways of working, remains open to feedback, and looks for opportunities to improve processes, outcomes, and how work gets done.
Judgment & Problem Solving
Uses critical thinking and sound judgment to solve problems, manage priorities, make informed decisions, and know when to act independently, involve others, or escalate.
Benefits
At Soho Studio/TileBar, each eligible employee receives a competitive total compensation package including Medical, Dental, Vision, Life Insurance, and Short-Term Disability in addition to a Company match on 401K, and Free Weekly Lunch. Our employees also receive Paid Time Off (PTO), Holidays, and Personal Days.
Compensation
$65,000 - 70,000 per year
Actual compensation is based on multiple factors that are unique to each candidate, including but not limited to skill set, level of relevant experience, and specific work location. Salary ranges for positions based in other locations may differ based on the cost of labor in that location.
At TileBar, we are committed to fostering a workplace that embraces and celebrates diversity, equity, and inclusion. We believe that a diverse and inclusive workforce not only enhances our ability to innovate and succeed but also creates a more enriching and supportive environment for our employees.
TileBar is an equal opportunity employer and welcomes applications from all qualified individuals, regardless of their race, color, religion, gender, sexual orientation, gender identity or expression, national origin, genetics, age, disability, or veteran status.
Salary Context
This $65K-$70K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2094 roles with salary data).
View full AI/ML Engineer salary data →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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At Soho Studio Corp, 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 in Demand for This Role
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 $212,160 based on 13,809 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $112,320. This role's midpoint ($67K) sits 68% below the category median. Disclosed range: $65K to $70K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Soho Studio Corp AI Hiring
Soho Studio Corp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $70K - $70K.
Location Context
AI roles in New York pay a median of $221,000 across 3,262 tracked positions. That's 4% above the national 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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