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About This Role
Location: Dallas preferred, close to where our inventory and operations run
Compensation: Competitive startup comp and meaningful equity upside
Department: Operations
Reports to: Director of Operations
*Also posted as: Senior Operations Analyst · Business Intelligence Analyst · Automation Lead · Data Analyst*
About Rare Candy
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Rare Candy is revolutionizing the $25 billion trading\-card industry as the only community marketplace 100% dedicated to TCGs. Collectors use our AI\-powered card scanner, data\-rich collection tools, and jaw\-dropping drops to buy, sell, rip, and showcase Pokémon, MTG, Lorcana, One Piece, and more. We've hit 10x revenue growth over the last 6 months, the business is highly profitable, and we're building the team to take this from a great product to a category\-defining one. We're a 10\-person team moving fast and looking for the next set of TCG\-obsessed rock stars to join our team.
The Role
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Running an operation \- the models, the dashboards, the inventory math, the recurring processes \- used to take an army of analysts. With AI, it takes one exceptional thinker to wield it.
We're hiring a Lead Operations Analyst to own the analytical and automation backbone of our operation. You'll run the daily and weekly inventory processes, own the dashboards our decisions depend on, and build the workflows and automations that take manual work off the whole team's plate. Your home base is operations and inventory, but the leverage you create will benefit the whole company, growth included.
You own the dashboards, the recurring workflows, and the automation buildout. Operations leadership owns the final inventory policy and the business tradeoffs.
Here's the specific kind of person we're looking for: someone who has the expertise to do the work by hand, but has figured out how to get massive leverage by building custom skills, workflows, and plugins that leverage AI tools and other modern tools to do the heavy lifting, and has the judgment to check it, catch what's wrong, and ship something they'd stake their name on. You know the fundamentals cold, which is exactly why you can trust yourself to move fast with AI and still get the numbers right. In a business where inventory accuracy is everything, that combination is the whole job.
What Success Looks Like
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First 30 days: You take over running our daily and weekly inventory processes, so they no longer depend on the founder or ops leadership to keep moving. You also identify and ship at least one workflow or automation that unlocks real, measurable value.
First few months: You own the operations dashboards end to end, so the team makes decisions off reliable, self\-serve data you maintain, and you've built a growing library of automations that remove manual hours across the operation.
By the end of year one, the analytical and automation engine of operations runs on you, not on the founder. Ops decisions are backed by data nobody has to scramble to assemble. You've shipped a real portfolio of time\-saving automations across ops and growth, and you've become the person who figures out how we put each new wave of AI tooling to work.
What You'll Do
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- Run the recurring engine: own the daily and weekly inventory processes end to end, reliably, without dropping balls.
- Own the dashboards: build and maintain the operational dashboards and data the team trusts to make decisions. Get the numbers right every time.
- Build leverage: identify high\-value bottlenecks and ship no\-code or low\-code workflows and automations that eliminate manual work across operations and growth.
- Orchestrate AI and verify it: use AI to do operational work at a fraction of the old cost, and bring the craft and rigor to check its output and catch what's wrong.
- Operationalize what's next: stay on the frontier of AI tooling and own how we put each new capability to work in our operation.
- Create leverage across the company: based primarily in operations, you'll also build tools and automations that help our growth team move faster.
A Day in Your Life at Rare Candy
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You start by running the morning inventory process, except 'running' now means kicking off a workflow you built, scanning the output for anything that looks off, and catching the one number that doesn't reconcile before anyone downstream sees it.
Midday, you spot that the team is hand\-assembling a report every week, so you spend two hours building the automation that kills that task forever.
After lunch, you refine a dashboard the ops team relies on, then test a new AI tool against a gnarly sourcing question, getting to an answer in twenty minutes that used to take an afternoon, and double\-checking it against the fundamentals before you trust it.
You Might Be a Fit If
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- You've got 3 to 6 years turning messy operational reality into reliable numbers and working systems. We care far more about what you've actually built and run than your exact years.
- You have genuine analytical craft. You're fluent in Excel or Sheets, you can do the analysis from first principles, and that's exactly why you can trust yourself to move fast with AI. SQL is a plus, not a requirement.
- You’re on the bleeding edge of new AI\-enabled tools and workflows. You default to having AI do the heavy lifting, and you have the judgment to verify its output, catch errors, and never ship a number you haven't checked.
- You build. You ship no\-code and low\-code automations and workflows (Airtable, Zapier or Make, scripts, dashboards, whatever gets it done) that measurably save people time.
- You're relentlessly detail\-oriented. In a business where inventory accuracy is the whole game, you're the person who catches the thing everyone else missed.
- You're scrappy and high\-agency. You figure it out, build the workaround, and don't wait to be handed a process or a tool.
- Required signal: you know the trading\-card or collector world. You collect, you play, or you've got real fluency in an adjacent collectible community.
What You'll Need to Thrive Here
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This is a build\-it role in a live operation, so you'll be maintaining and upgrading the engine simultaneously. Some days are heads\-down dashboard and automation work; others are firefighting a number that has to be right before the team acts on it.
You'll have a lot of autonomy over how you build, while final inventory policy and the business calls stay with ops leadership.
If you love removing manual work, you trust your own verification, and you want to own how a fast\-scaling operation uses AI, this is a great fit.
Why Join Us Now
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- Own the analytical and automation backbone of a fast\-scaling operation, with real scope from day one.
- Work at the frontier: this role is built on the bet that AI changes what one great operator can do. You'll get to prove it, every day.
- Build the systems the whole company runs on, and grow with a fast\-scaling operation.
- Competitive startup comp and meaningful equity upside.
- Full benefits: health, dental, and vision coverage, 401(k), and a wellness stipend.
- A monthly dogfooding budget to spend on cards, because we hire people who love this hobby.
How to Apply
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Apply through our Ashby posting and answer these:
- Describe a messy operations or reporting problem you turned into a reliable process, dashboard, or automation. What was broken, what did you build, and what improved?
- What tools have you used to build operational systems? Include Excel or Sheets, Airtable, automation tools, scripts, dashboards, AI tools, or anything else relevant.
- Give one example of using AI to move faster. How did you verify the output before trusting it?
- Tell us about a time you caught a data, inventory, or process error before it caused damage.
- Required: what trading\-card or collector communities do you understand best? Tell us your favorite card, set, format, or collector niche.
*Rare Candy is an equal opportunity employer. We celebrate individuality, from Pikachu collectors to Planeswalker pros, and are committed to building an inclusive team.*
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 Rare Candy, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Rare Candy AI Hiring
Rare Candy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
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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