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About This Role
Description
Company and Vision
PlanetArt's vision is to be the leading seller of personalized and make\-on\-demand products worldwide. We provide consumers with unmatched tools and content and an unparalleled end\-to\-end customer experience that result in high\-quality, meaningful finished products and memorable celebrations of life events.
The company's brands include the popular FreePrints and FreePrints Photobooks apps and the industry leading Simplytolmpress card and stationery site, as well as Personal Creations, Cafe Press and ISeeMe! Visit planetart.com to learn more about our brands.
We have more than 500 team members across multiple offices, primarily in Calabasas CA, San Diego CA, Woodridge IL, Minneapolis, MN and Pleasanton, CA. We also have team members in two company\-owned offices in China, as well as in Europe.
Job Overview
PlanetArt is seeking an AI Product Engineer, Internal Tools to help build the next generation of AI\-powered internal tools, prototypes, automations, and workflows across the company.
This is a hands\-on builder role working directly with the Head of AI, engineering team members, and business departments across PlanetArt. The ideal candidate is a highly effective AI\-native builder who can use tools like Cursor, Claude Code, Codex, ChatGPT, and other modern AI development workflows to quickly turn ambiguous business problems into useful software.
This role is not just about taking requests and building exactly what was asked for. We are looking for someone who can work directly with internal users, understand the real workflow problem, ask sharp questions, prototype quickly, translate feedback into better tools, and know when to simplify, iterate, or push back. Strong UX instincts, technical judgment, communication skills, and a high\-agency mindset are essential.
IMPORTANT: A public portfolio is required. Candidates must include links to publicly reviewable portfolio work, such as live apps, websites, GitHub repositories, demos, case studies, or shipped tools. Applications that do not include portfolio links will not be considered.
PLEASE NOTE: Candidates must be local to or willing to relocate to the Calabasas area as we operate on a hybrid work model (3 days onsite, 2 remote).
What You'll Do
Key Responsibilities
- Build AI\-powered internal tools, prototypes, dashboards, automations, and workflow applications for teams across PlanetArt
- Work directly with departments and internal users to understand pain points, observe workflows, gather feedback, and translate needs into practical software solutions
- Use AI\-assisted development tools such as Cursor, Claude Code, Codex, ChatGPT, and similar platforms to rapidly prototype and iterate
- Partner closely with the Head of AI and a small team of software engineers to identify high\-impact opportunities for internal tooling and AI enablement
- Create functional prototypes quickly, validate them with users, and refine them based on real feedback and usage
- Design and improve prompts, prompt chains, structured outputs, agent workflows, and AI\-assisted processes for internal business use cases
- Build lightweight full\-stack applications, scripts, integrations, and automations using modern web and AI development tools
- Balance speed with quality by knowing when a prototype is good enough, when it needs hardening, and when engineering support is required
- Translate vague or incomplete stakeholder requests into clear requirements, user flows, acceptance criteria, and implementation plans
- Improve user experience through thoughtful workflow design, clear interface choices, concise copy, and practical usability improvements
- Document tools, workflows, prompts, assumptions, and handoff notes so internal teams and developers can understand and maintain what was built
- Stay current with emerging AI coding tools, prototyping platforms, LLM capabilities, and applied AI workflows
Requirements What You Should Have
Skills, Qualifications, and Requirements
- Publicly reviewable portfolio work is required; applications without portfolio links will not be considered
- Demonstrated ability to build and ship functional apps, websites, prototypes, automations, or internal tools
- Strong hands\-on experience with AI\-assisted development tools such as Cursor, Claude Code, Codex, GitHub Copilot, ChatGPT, v0, or similar tools
- Practical full\-stack development ability, especially with modern web technologies such as JavaScript, TypeScript, React, Next.js, Python, APIs, databases, or similar tools
- Strong prompt engineering skills, including the ability to design, test, refine, and document prompts for real business workflows
- Ability to work directly with non\-technical users, gather feedback, identify the real problem, and convert that feedback into product improvements
- Strong UX and product instincts, with the ability to make tools simple, useful, and easy for internal teams to adopt
- Technical judgment to review AI\-generated code, debug issues, avoid fragile implementations, and escalate appropriately when production engineering support is needed
- Excellent written and verbal communication skills with technical and non\-technical audiences
- High agency, strong ownership, and comfort operating in ambiguity
- Ability to manage multiple small projects, prioritize high\-impact work, and move quickly without losing sight of quality
- Bachelor's degree in Computer Science, Information Technology, Human\-Computer Interaction, Design, Analytics, or a related field preferred; equivalent practical experience and exceptional portfolio work will be considered
Portfolio Requirement
- A strong candidate portfolio may include:
- Live apps, websites, or internal\-tool\-style projects with meaningful functionality
- GitHub repositories showing code quality, iteration history, and technical decision\-making
- AI\-powered tools, LLM workflows, prompt systems, automations, or agent\-based applications
- Case studies showing how user feedback was translated into product changes
- Examples of rapid prototypes that solved real business, workflow, operational, creative, or ecommerce problems
- Projects with real UX depth, not just landing pages, toy demos, or tutorial clones
- Candidates should be prepared to walk through their portfolio, explain what they built, what tools they used, what tradeoffs they made, and how they improved the work based on feedback.
Bonus Qualifications
- Experience building internal tools for business operations, ecommerce, marketing, creative, customer service, analytics, or product teams
- Experience with AI\-assisted product development workflows, rapid prototyping, and vibe coding methodologies
- Experience with LLM APIs such as OpenAI, Anthropic, Google Gemini, or similar platforms
- Familiarity with structured outputs, RAG, vector databases, embeddings, evals, AI agents, or tool\-calling workflows
- Experience with low\-code or rapid development platforms such as Retool, Supabase, Firebase, Vercel, Airtable, n8n, Zapier, Make, or similar tools
- Experience designing simple interfaces, workflows, or prototypes in Figma or similar tools
- Experience working with ecommerce, consumer products, personalization, creative production, or make\-on\-demand businesses
- Experience collaborating with software engineers and preparing prototypes for production handoff
What You Can Expect
Working Conditions
- Work is performed in an office environment with low to moderate noise levels.
- Occasional lifting of up to 20 pounds.
- Position requires regular, continuous use of computer.
- Position requires regular sitting and standing.
- Position requires regular interaction with team members through the following methods: in\-person, phone, Zoom, or email.
- May require occasional travel.
- This is a hybrid position; employees are expected to be in the office three days per week (Monday, Tuesday, and Thursday) with the option of working remotely two days (Wednesday and Friday).
Benefits
The compensation range for this position is $110,000 \- $130,000 annual salary.
PlanetArt offers a comprehensive benefits package, including:
- Health, Dental, and Vision Insurance
- Life Insurance
- 401(k) with matching
- Pet Insurance
- Mental Health Benefits
- Comprehensive Time Off Program, including Vacation, Sick Days, Paid Holidays, and Floating Holidays
- Employee Product Discounts
Salary Context
This $110K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At PlanetArt, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($120K) sits 34% below the category median. Disclosed range: $110K to $130K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
PlanetArt AI Hiring
PlanetArt has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Calabasas, CA, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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