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About This Role
We are in relentless pursuit of an equitable and inspiring workplace that is respectful of all, reflects and represents the world in which we live, and fosters trust, collaboration and belonging. Consistent with this approach, we hire the best qualified candidates for all positions.
We are looking for an Executive Vice President to join Edelman's technology public affairs team in Washington DC or New York City . Edelman’s Public & Government Affairs team is experiencing significant growth in the technology policy and issues management space. As the global elections unfold, we’ve been working with domestic clients, industry associations and multinational organizations to develop strategies to navigate the shifting sands of technology policy, while also developing programs to promote and protect their license to operate.
We’re helping Fortune 500 companies re-conceive their business models and partnering with start-ups to challenge entire markets. Technology is one of Edelman's fastest growing sectors and as the global technology policy landscape evolves, we aim to be the foremost partner for business in shaping the future.
Our technology public affairs team is helping those companies navigate the increasingly challenging legislative, regulatory and reputational landscape. We are a group of former government officials, campaign strategists, Capitol Hill staffers and technology experts who understand the issues and how to help organizations look around corners and shape favorable policy outcomes in Washington and beyond.
We’re looking for someone with a firm grasp of the major issues impacting global technology companies and innovators, understand how issues impact brands, and how brands can protect and advance their reputations by proactively advancing their policy objectives. You should have a good understanding of the policy media landscape and of the third parties working to shape policy discussions. And of course, we are looking for you to be a consummate collaborator, able to work with different disciplines across Edelman’s multiple offerings. Specifically, we’re looking for an innovative leader who thoroughly understands the dynamics of the policy landscape in AI, accountability, automation, data privacy, cyber security and the role technology plays in economic and national security among other topics.
What you'll do
- Work in partnership with clients and prospects to develop comprehensive strategies to successfully navigate domestic and international political, policy and reputational risk.
- Deliver superior written communications for external and internal audiences
- Lead the strategic development and execution of comprehensive earned, owned an paid media campaigns to shape favorable outcomes for clients.
- Lead multiple client accounts/projects with a focus on setting strategic direction and plans to help clients achieve policy goals
- Drive new business growth by identifying prospects and leading proposals
- Collaborate with senior leadership in identifying and developing opportunities for growth
- Partner with Edelman marketing to produce intellectual property and events
- Build relationships with clients and colleagues at all levels
- Supervise team members by motivating, coaching and consistently providing feedback
- Serve as daily client contact, addressing timely client issues thoughtfully and effectively
- Build and manage relationships with third parties and internal business partners to meet client needs; ensuring operational excellence as an extension of internal team
- Commit to continuous learning; set and pursue stretch goals
Basic Qualifications:
- Possess a minimum of 12 years of experience in public relations, technology policy, government or tech media and/or related fields including policy/legislative research, issue advocacy, management consulting, think tanks and publishing.
- Able to work a hybrid schedule with 3 days a week in the DC or New York City office and/or on-site with clients.
Preferred Qualifications:
- Avid consumer of political, policy and technology media
- Have experience working in technology, with a specific focus on technology policy issues
- Public affairs or Capitol Hill experience preferred
- Have effectively managed multiple sizable accounts, including financials
- Have driven new business proposals (and love doing so)
- Have served as the daily client contact and demonstrate solid client interaction skills
- Have superior written and verbal communications skills
- Proven ability to consistently make sound judgments
- Effectively motivate and manage teams and collaborate well
- Perform well under pressure and effective at managing competing priorities
$185,000 to $290,000 a year
#LI-MB
An employee’s pay position within the salary range will be based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, travel requirements, revenue-based metrics, any contractual agreements, and business or organizational needs. The range listed is just one component of DJEH’s total compensation package for employees. Other rewards may include annual bonuses, a Paid Time Off policy, and region-specific benefits.
DJEH offers a wide range of benefits: medical and dental insurance, vision, 401K, life insurance, disability insurance, paid time off, travel assistance and wellness programing.
DJEH is proud to be an equal opportunity employer and believes in diversity, equity, and inclusion. We seek applications from all qualified candidates without regard to race, color, gender, sex, age, religion, physical or mental disability, military and veteran status, or any other basis protected by federal, state or local law. If you require a reasonable accommodation in any part of the employment process, please let us know.
Salary Context
This $185K-$290K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 33,423 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Edelman, 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 $154,000 based on 8,743 positions with disclosed compensation. This role's midpoint ($237K) sits 54% above the category median. Disclosed range: $185K to $290K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Edelman AI Hiring
Edelman has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $290K - $290K.
Location Context
AI roles in New York pay a median of $204,100 across 1,633 tracked positions. That's 7% above the national 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 33,423 open positions tracked in our dataset. By seniority: 3,283 entry-level, 20,769 mid-level, 6,381 senior, and 2,990 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,320 positions). The remaining 30,984 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 33,423 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (30,275), AI Software Engineer (749), AI Product Manager (741). 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 (3,283) are outnumbered by mid-level (20,769) and senior (6,381) 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 2,990 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,320 positions), with 30,984 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (21,235 postings), Aws (11,126 postings), Rust (9,803 postings), Python (4,999 postings), Azure (3,220 postings), Gcp (2,707 postings), Prompt Engineering (1,817 postings), Openai (1,487 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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