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Who We Are
CompTIA is a leading voice and advocate for the $5 trillion global information technology ecosystem; and the estimated 75 million industry and tech professionals who design, implement, manage, and safeguard the technology that powers the world’s economy.
Through education, training, certifications, philanthropy and market research, CompTIA promotes industry growth; the development of a highly skilled workforce and a commitment to creating an environment where innovation happens, and the opportunities and benefits made possible through technology are available to all.
Scope Summary
Reporting directly to the General Counsel, this individual serves as CompTIA's primary subject matter expert on AI\-related legislation, regulation, and policy as it pertains to workforce training, technology competencies, and credentialing. The Director monitors and translates the rapidly evolving AI governance landscape into actionable intelligence and commercial opportunity for the organization. As a secondary function, this role also supports CompTIA partners in identifying and pursuing federal, state, and private funding opportunities aligned with workforce development and technology training priorities.
This role requires deep policy fluency, analytical rigor, and the ability to translate complex regulatory developments into strategic and commercial insights. The Director will work cross\-functionally with Sales, Marketing, and Product teams to identify and capitalize on opportunities created by the evolving AI policy environment.
Essential Duties and Responsibilities
AI Policy Intelligence \& Commercialization
- Monitor and analyze federal and state AI legislation, executive orders, agency guidance (NIST, EEOC, FTC, DOL, etc.), and international regulatory developments (EU AI Act, etc.) as they relate to AI competencies, workforce training, and credentialing.
- Synthesize regulatory developments into actionable intelligence for internal stakeholders — translating policy signals into product, sales, and marketing implications.
- Partner with Product, Sales, and Marketing teams to identify opportunities to align CompTIA offerings (certifications, training, content) with emerging compliance requirements and government procurement priorities.
- Develop and maintain an internal AI policy tracker and regularly brief senior leadership on material developments.
- Represent CompTIA at relevant industry coalitions, working groups, and public comment processes.
Strategic Funding \& Grant Development
- Research, identify, and monitor federal, state, and private grant and funding opportunities relevant to CompTIA partners, with particular attention to workforce development, technology training, and digital skills initiatives.
- Serve as a ghostwriter and strategic advisor for CompTIA partners pursuing grant funding — developing compelling, compliant grant narratives, needs statements, budgets narratives, and supporting materials.
- Maintain a pipeline of active and prospective funding opportunities, tracking deadlines, eligibility requirements, and award cycles.
- Build and maintain relationships with program officers, agency staff, and foundation contacts to stay ahead of funding trends and priorities.
- Develop internal templates, toolkits, and best practice resources to scale grant support across the partner network. Track and report on grant outcomes, award rates, and partner feedback to continuously improve the program.
Key Performance Metrics
- Successful ongoing analysis and mapping of material AI legislative, policy, and regulatory developments worldwide.
- Partner grant application success rate and total funding secured by partners supported through this role.
- Increased references and correlation between product marketing and regulatory frameworks.
Skills and Qualifications
- Strong working knowledge of the federal and state policy landscape around AI, technology training, and workforce development — including familiarity with key regulatory bodies (NIST, EEOC, FTC, DOL) and international frameworks such as the EU AI Act.
- Ability to work cross\-functionally and translate complex regulatory content into business\-relevant insights for product, sales, and marketing stakeholders.
- Exceptional written communication skills — ability to synthesize policy developments into clear executive briefings, public comments, and partner\-facing materials.
- Articulate public speaking skills.
- 3\+ years of experience in public policy, government affairs, regulatory affairs, or a closely\-related field.
- Experience supporting grant or funding pursuits — ideally in workforce development, education technology, or tech\-adjacent nonprofit/association environments — is a plus.
Education and/or Experience
- Bachelor's degree in public policy, political science, communications, law, or a related field.
- 6\+ years of direct experience in related field and/or advanced degree
- Juris Doctor degree a plus.
Why Join Us
We're a team of driven, creative problem\-solvers who are passionate about empowering people to realize their full potential. We foster a collaborative and inclusive culture where ideas are welcomed, growth is encouraged, and success is celebrated. We're not just looking for employees, we're looking for partners who share our vision and are eager to contribute to our purpose. If you're ready to unlock your potential and reach new heights, join us today.
Our team works hard and we recognize the importance of taking care of our own. We offer our employees a comprehensive suite of benefit offerings including:
- Health, Dental, and Vision Insurance \& FSA/HSA Plans
- Performance bonus up to 15% of base salary
- Unlimited PTO \& 15 Paid Holidays
- Flexible Schedules \& Summer Hours
- 12 weeks of Paid Parental Leave
- Sponsored Costco or Sam’s Membership
- 401K Retirement Plan with 6% company match
- Spot Bonuses for going above \& beyond
- Tuition Reimbursement
- Home Office Allowance
- Wellness Reimbursement
- Student Loan Repayment
- Broadband Stipend
- Expected compensation based on experience and qualifications – $124,000 \- $140,000
CompTIA seeks excellence through diversity in its staff. We prohibit discrimination based on race, color, religion, sex, age, national origin, sexual orientation, gender identity or expression, disability, veteran status, or marital status.
CompTIA Careers Page
Salary Context
This $124K-$140K 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 CompTIA, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($132K) sits 27% below the category median. Disclosed range: $124K to $140K.
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.
CompTIA AI Hiring
CompTIA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $140K - $140K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 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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