Interested in this AI/ML Engineer role at U.S. Chamber of Commerce?
Apply Now →Skills & Technologies
About This Role
About Us:
If you are passionate about the ability of American business to improve lives, solve problems, and strengthen society, the U.S. Chamber of Commerce is the place for you. As the world’s largest business organization, we believe in building a future that gives everyone the opportunity to pursue a better tomorrow. We make it our job today–and every day–to build the strongest relationships possible among the American people, business leaders, and elected officials in Washington, D.C., state capitals, and countries around the globe. For them and the businesses we represent, the U.S. Chamber is a trusted advocate and partner.
We are driven by the pursuit of innovation and partnership and hold ourselves to the highest standards. Our commitment to our members is matched only by our commitment to our employees. As part of our team, we will support your long-term career development while delivering relevant learning opportunities. We will empower you to lead, develop deep expertise, and find new approaches to solving the toughest challenges.
Position Overview:
The Vice President, Federal Affairs at the U.S. Chamber Institute for Legal Reform (ILR) serves as a senior leader responsible for advancing the Chamber’s legal reform agenda through strategic policy development, advocacy, and stakeholder engagement. Reporting to the senior vice president of Legal Reform Policy at ILR, this role plays a critical part in shaping and executing initiatives that promote a fair, predictable, and constitutionally grounded civil justice system.
The ideal candidate brings deep expertise in federal law, regulatory oversight, and congressional processes, paired with strong strategic judgment and the ability to operate effectively across Congress, executive agencies, the legal community, and the business sector.
Responsibilities:
- Lead the development and execution of legal reform strategies across federal legislative and executive agency policy arenas, with particular focus on legal reform, administrative law, civil enforcement, and rule-of-law principles impacting the business community.
- Advise senior Chamber and ILR leadership on emerging federal legal, legislative, regulatory, and political developments regarding liability issues pertaining to the business community.
- Direct ILR advocacy efforts before Congress, executive agencies, and the administration and support legislative strategy, hearings, and oversight engagement.
- Draft and review policy materials, including legislation, regulatory comments, white papers, legal analyses, and public-facing communications.
- Serve as a primary liaison to lawmakers, congressional committees, administration officials, legal organizations, member companies, and coalition partners.
- Manage and coordinate complex multi-stakeholder initiatives, task forces, and external coalitions aligned with ILR priorities.
- Represent the Chamber and ILR in meetings, briefings, and public forums, articulating policy positions clearly and persuasively.
- Collaborate closely with the Chamber’s Legal, Government Affairs, Communications, and Federation teams to ensure integrated advocacy and messaging.
Qualifications:
- Juris Doctor and admission to at least one U.S. jurisdiction.
- 15 or more years of experience in public policy, constitutional law, regulatory oversight, or congressional leadership roles.
- MBA or demonstrated business acumen is a plus.
- Demonstrated expertise navigating congressional committees, legislative drafting, hearings, and oversight processes.
- Strong written and oral advocacy skills, including experience producing legal analysis, policy memos, and testimony.
- Proven ability to manage complex policy initiatives and influence outcomes in politically sensitive environments.
- Ability to exercise sound judgment, discretion, and strategic thinking in high-stakes matters.
- Experience working with national organizations, trade associations, or advocacy groups is preferred.
The salary range for this position is $216,574.00 to $250,000.00. The actual salary paid for this position will vary based on market data, an applicant’s qualifications, relevant degrees, certifications, and other factors. Candidates with more advanced experience are encouraged to apply, as the role may be calibrated at a higher level based on experience and organizational need. Our full-time employees are eligible for benefits, including health care, vision, dental, retirement, and paid leave.
We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
#LI-Hybrid
Salary Context
This $216K-$250K 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At U.S. Chamber of Commerce, 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 ($233K) sits 51% above the category median. Disclosed range: $216K to $250K.
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.
U.S. Chamber of Commerce AI Hiring
U.S. Chamber of Commerce has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $250K - $250K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.