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About This Role
APPLY HERE: https://reioptimize.notion.site/e417d89544a28299a2b081754c241a00?pvs\=105
Job Title: Director of AI Curriculum
Company: The Uncommon Business
Employment Type: Full\-Time, W\-2
Location: Remote with onsite in Minneapolis, MN
Compensation: $130,000 – $170,000 per year (depending on experience), plus incentive compensation based on KPI metrics
Reports to: VP of Product and Innovation
About the Role
The Uncommon Business is an AI\-first education company that teaches business leaders to architect intelligent systems. As Director of AI Curriculum, you own what TUB teaches 5,000\+ founders about AI, you lead the team that builds it, and you hold the standard everything ships against.
Most curriculum roles hand you a content calendar and ask you to fill it. This one hands you a discipline and a team and asks you to own both. You decide what TUB teaches, the methodology it teaches by, and which tools and models earn a place in front of a founder — and which get cut. You don't build most of it yourself: you lead a Curriculum Manager and a team of Producers who create to the standard you set, and you spend your time on direction, quality, and the calls only you can make.
The model landscape moves every week, and a tool that was the answer in March can be dead weight by June. You're the person who tracks that across every domain we teach, decides what the curriculum does about it, and directs a team to ship the answer fast and codify it so it stops depending on any single person. You still teach live on camera, selectively, where your authority moves the room. This is a leadership seat: your leverage is the team you build and the bar you hold, not the volume you personally produce.
What You'll Do
- Curriculum strategy and roadmap. Own what TUB teaches across the full curriculum, what comes next, and what gets cut. The roadmap is yours to set, sequence, and defend, aligned to where the business and market are heading.
- Team leadership and development. Lead the Curriculum Manager and, through that seat, the Producers who build the work. Set priorities, hold accountability to deadlines and quality, develop your people, and delegate all creation so your time goes to direction.
- Methodology, IP, and quality standard. Write and hold the standard the team creates against, and codify methodology into published, transferable IP so it no longer lives in one person's head.
- AI research and validation. Live at the front edge of new tools and models, rule on what's real versus hype before it reaches a student, and own the process that keeps the curriculum current as the landscape moves.
- Live teaching and domain authority. Teach live on camera selectively, in the moments where your authority carries the room, as the visible face of the curriculum's credibility.
What We're Looking For
- You lead through a team — you can set a standard, delegate the work, hold people accountable, and resist the pull to do it all yourself.
- You've built methodology or curriculum yourself and can point to what you made.
- You know the AI landscape cold and keep pace as it changes; you build with Claude Code and the current toolset and can explain why one approach beats another.
- You're an AI expert first and a teacher by instinct — you can make a hard idea simple without making it shallow, and teach your team to do the same.
- You're comfortable teaching live on camera to thousands and get sharper, not shakier, when the lights are on.
- You write and edit at a publish\-ready bar and can raise a Producer's draft to that bar with clear, specific direction.
- You hold strategy and execution in the same week.
What Success Looks Like
- The roadmap is set, sequenced, and defended a cohort ahead, with clear teach\-or\-cut calls.
- Your team ships publish\-ready frameworks on cadence, without you as the bottleneck.
- Your Curriculum Manager and Producers own more of the build over time, requiring less rework from you by month six.
- At least 4 new AI tools or models evaluated and ruled on per month.
- Live teaching that lands, with 90%\+ cohort satisfaction on the segments you own.
- The curriculum fully written down and transferable within your first 6 months.
- The coaching team delivering against your documented standard, verified by spot\-checks each cohort.
Launch Season Expectations
Twice per year (10–12 weeks total), team members in launch\-critical roles are expected to be available from 7 AM to 7 PM, with additional hours often required for preparation, live event support, and post\-launch debriefs. This pace is intentionally high\-intensity. Outside launch seasons, workload is sustainable and strategic.
Benefits
- Monthly wellness stipend plus personalized and group wellness coaching
- $1,500–$3,000 annual professional development budget
- Coworking membership allowance OR home office setup budget — your choice
- Unlimited PTO with real boundaries
- Companywide closure December 25 – January 1
- Incentive Compensation Program based on KPI metrics
How to Apply
Applications are initially reviewed using AI\-assisted screening tools to assess alignment with role requirements. All hiring decisions are made by humans.
The Uncommon Business is an equal opportunity employer.
Pay: $130,000\.00 \- $170,000\.00 per year
Work Location: Remote
Salary Context
This $130K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The Uncommon Business, 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. This role's midpoint ($150K) sits 30% below the category median. Disclosed range: $130K to $170K.
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.
The Uncommon Business AI Hiring
The Uncommon Business has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $170K - $170K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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.
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