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About This Role
Gravity Research is hiring a Senior AI Transformation Analyst to help some of the world’s leading communications and corporate affairs teams navigate the rapidly changing world of AI.
This is a role for someone who is endlessly curious about what AI can do, eager to experiment with the latest tools, and excited to turn emerging ideas into practical solutions. You’ll work alongside senior executives to help organizations move from simply experimenting with AI to fundamentally changing how their teams work.
Gravity Research, born of National Journal, is a premier custom research and insights organization supporting corporate, association, and nonprofit clients. Our mission is to empower organizations to anticipate risk, spot opportunities, and rationalize reputational concerns against a backdrop of evolving public pressures.
Background
We are looking for curious and innovative researchers and consultants interested in helping communications and corporate affairs leaders smartly navigate AI’s transformation of their function.
The Role
Senior AI Transformation Analysts are responsible for contributing to all aspects of Gravity’s new AI advisory practice. Analysts help communications and corporate affairs leaders move their teams from early experimentation to mature AI adoption. Analysts on this team use qualitative research methods to assess where organizations stand in their adoption journey, create change management deliverables, and build step\-by\-step case studies and practical implementation guidance on how to modernize the function. Analysts work directly with senior leaders at Gravity and senior communications and corporate affairs executives at member organizations.
Qualifications Of The Ideal Candidate
The ideal candidate will have strong research instincts and hands\-on comfort with AI tools and prompting. The candidate will have the ability to execute against tight deadlines to develop professional and intellectually sound deliverables for members, both independently and in close collaboration with team members.
### Your Responsibilities Will Include:
- Helping communications teams at Fortune 500 corporations understand how to adopt, govern, and scale AI within their organizations.
- Developing research using qualitative methods to synthesize findings from interviews, surveys, and open source research into clear, actionable insights.
- Building step\-by\-step case studies and roadmaps that translate peer and expert insights into practical implementation plans clients can act on.
- Constantly experimenting with new AI tools and releases to understand first\-hand how they can be used to improve communications workflows.
- Applying change management principles to help clients structure adoption roadmaps, training plans, and upskilling programs for teams navigating AI\-enabled ways of working.
- Drafting deliverables, including PowerPoint presentations, memos, and other formats, to communicate findings and recommendations to clients.
- Supporting the design and delivery of monthly webinars, executive briefings, and training sessions that build AI capability across a client's team.
- Preparing briefing materials and research summaries for executive advisors ahead of member meetings, and responding to on\-demand research requests as questions arise.
- Contributing to product development by crafting new and innovative approaches to producing the analytic work, optimizing operations, and perfecting the research product.
- To perform this job successfully, each essential competency and responsibility must be performed satisfactorily. Reasonable accommodations may be made to enable an individual with disabilities to perform essential functions. Other duties may be assigned to meet organizational goals.
### Specific Qualifications Include:
- A highly analytic, natural problem solver.
- The ability to highlight key insights within a complex set of data, synthesize large amounts of information, and then clearly explain these insights to clients.
- Creative thinking and resourcefulness in problem\-solving.
- A strong writer able to clearly frame issues for a variety of audiences; some experience with PowerPoint or other visual presentation formats highly preferred.
- A skilled project manager, able to independently manage competing priorities and adhere to timelines.
- Familiarity with change management fundamentals and the ability to apply them practically to drive team adoption of new tools and workflows.
- Hands\-on experience using AI tools and prompting (e.g., ChatGPT, Claude, Gemini, Copilot).
- An individual with 1\-3 years of experience in at least one of the following domains: strategy consulting, change management, organizational development, AI transformation, or a related field.
- Research focused on AI, organizational development, or the future of work preferred.
- Earned Bachelor's degree required.
- Entrepreneurial spirit; Track record of building new initiatives from conception to execution.
- Prodigious work ethic and spirit of generosity.
The salary range for this role is $70,000\-$85,000, commensurate with experience.Start Date: Available immediately
Application Deadline: August 28, 2026
Employment Type: Full\-time
Location: This job is based in Washington, DC. Gravity Research operates on a hybrid schedule, with employees required to be in the office every Tuesday, Wednesday, and Thursday. During the month of August 2026, Gravity Research teams will be fully remote.
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About Us
Across Gravity Research, generally, the firm looks for three “pillar gifts” in you, and everyone else. In all of us, these are more aspirational than actual, but they are central in our intentions –
Force of Ideas: At the center of Gravity Research’s work are the ideas within our work. We believe that ideas—the good and not—have consequences. Our ultimate objective is to bring rigor, insight, and intellectual honesty to the goal of separating the bad from the good, and giving voice, argument, and flight to maintaining the latter.
Spirit of Generosity: Gravity Research seeks in its ranks a spirit of generosity: a natural disposition in each colleague toward service and selfless conduct. Our writing should be cut from the same cloth—critical on the merits, but informed by charity and forbearance in measuring motive and personal character.
Radical Responsibility: We give our people a lot of responsibility early on, and our top performers embrace it. We believe in holding ourselves and our colleagues accountable, taking ownership over big things, striving for excellence, and celebrating success.
Gravity Research is an Equal Opportunity Employer. We do not discriminate against our applicants because of race, color, religion, sex (including gender identity, sexual orientation, and pregnancy), national origin, age, disability, veteran status, genetic information, or any other status protected by applicable law.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $70K-$85K 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 Journal, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($77K) sits 64% below the category median. Disclosed range: $70K to $85K.
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 Journal AI Hiring
National Journal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $85K - $85K.
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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