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Description:
The National Retail Federation is a trade association that advocates for the people, brands, policies and ideas that help retail succeed. From its headquarters in Washington, D.C., NRF empowers the industry that powers the economy. Retail is the nation’s largest private\-sector employer, contributing $5\.3 trillion to annual GDP and supporting more than one in four U.S. jobs. For over a century, NRF has been a voice for every retailer and every retail job, educating, inspiring and communicating the powerful impact retail has on local communities and global economies.
The National Retail Federation is seeking a Senior Manager of AI and Technology Policy to manage key technology policy projects and support NRF’s advocacy, member engagement, and thought leadership efforts on artificial intelligence, cybersecurity, privacy, fraud prevention, and emerging technology issues impacting the retail industry.
Reporting to the Vice President, AI and Technology Policy, this position will manage assigned technology policy and digital risk priorities, serve as the lead on select projects, presentations, and member resources, and work cross\-functionally to advance NRF’s policy priorities. The Senior Manager will regularly collaborate internally and liaise externally with member companies, coalition partners, policymakers, regulators, and other stakeholders on artificial intelligence, cybersecurity, fraud prevention, privacy, and emerging technology issues that support innovation, consumer trust, and responsible technology adoption.
This position is ideal for a curious, analytical, and organized policy professional who can independently manage complex projects, translate technical and policy developments into clear work products, and build expertise at the intersection of public policy, emerging technology, and retail innovation.
Essential Functions
Policy Research and Advocacy Support
- In partnership with the Vice President, manage key elements of NRF’s advocacy efforts related to AI, cybersecurity, fraud prevention, privacy, and other emerging technology issues.
- Support the development of policy positions, advocacy materials, comment letters, legislative analyses, testimony, research summaries, and member resources that align with NRF’s technology policy priorities.
- Serve as a day\-to\-day liaison with policymakers, regulators, member companies, coalition partners, and external stakeholders on assigned policy matters.
Member Engagement and Industry Leadership
- Manage the development and execution of technology, cybersecurity, fraud prevention, and digital risk research projects and member resources through NRF’s CDRI.
- Support strategic planning and make recommendations to strengthen CDRI initiatives, member engagement, and NRF technology policy efforts.
- Regularly engage with key NRF member groups, including the IT Security Council, AI Committee, and Fraud Prevention Professionals Working Group.
- Lead planning and execution for assigned meetings, roundtables, and conferences, including agendas, briefing materials, presentations, and member deliverables.
- Identify emerging technology challenges and opportunities facing retailers and translate complex insights into NRF policy priorities, member programming, and practical resources.
Requirements:
Minimum Qualifications
- Bachelor's degree required; public policy, political science, economics, or related field preferred.
- At least 4 years of relevant professional experience in government affairs, public policy, technology policy, cybersecurity, privacy, fraud prevention, or related fields.
- Strong writing, analytical, and policy communication skills, with the ability to synthesize complex and rapidly evolving technology issues into clear, actionable insights for business leaders, policymakers, and member audiences.
- Demonstrated knowledge of, or experience working on, public policy issues related to artificial intelligence, cybersecurity, privacy, fraud prevention, or emerging technologies impacting the retail industry.
- Strong organizational and project management skills with the ability to independently manage complex projects, timelines, meetings, and deliverables.
- Strong communication and relationship\-building skills, including the ability to collaborate cross\-functionally and liaise externally with members, partners, and other stakeholders.
- Ability to apply professional\-level knowledge, independently identify and analyze complex matters, and develop sound recommendations on emerging technology policy issues.
Preferred Qualifications
- Experience in one or more of the following environments is strongly preferred: congressional offices, federal agencies, state government, trade associations, consulting firms, retail companies, technology companies, think tanks, or research organizations.
Location: Washington, D.C. (In\-office 3 days a week)
How to Apply: Interested candidates are invited to submit an application with their resume and cover letter.
*National Retail Federation (NRF) is an Equal Opportunity Employer. We provide equal employment opportunities to all employees and applicants without regard to any legally protected characteristic. This applies to all employment decisions, including recruiting, hiring, pay, promotion, development, and termination.*
Salary Context
This $100K-$115K range is in the lower quartile 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 National Retail Federation Inc, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($107K) sits 50% below the category median. Disclosed range: $100K to $115K.
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.
National Retail Federation Inc AI Hiring
National Retail Federation Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $115K - $115K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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.
Frequently Asked Questions
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