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About Lawfare Institute
Lawfare is a non\-profit multimedia publication dedicated to “Hard National Security Choices.” We provide non\-partisan, timely analysis of thorny legal and policy issues through our written, audio, and other content—all of which you can find free of charge at our website, www.lawfaremedia.org. We strive to achieve academic\-level depth with magazine\-level readability at the pace of news. We aim to improve the discourse on the law and policy of national security with a relentless focus on substantive issues that matter—in a fashion that is useful to policymakers and practitioners, but also accessible to anyone who wants to access it. Our areas of coverage range from national security law, threats to democracy, cybersecurity, executive powers, content moderation, domestic extremism, and foreign policy, among many others.
About the role
We are pleased to announce that we are now accepting applications for an associate editor focused on the growing body of work at Lawfare exploring how artificial intelligence increasingly intersects with national security relevant domains, as well as law and policy. If you’ve ever listened to our podcasts or read our articles and thought, “I want to be part of that team,” now is your chance.
This is a highly demanding job. The associate editor plays a pivotal role in the editorial process, ensuring that the articles, documents, podcasts, and everything else we produce meet Lawfare’s editorial standards. This position focuses specifically on Lawfare’s new AI Research Program, which bridges technical, legal, and policy expertise to inform public debate across six research domains: AI governance and regulation; AI and national security; AI and the economy; AI and the legal system; AI and democratic institutions; and AI safety and catastrophic risk. The associate editor supports the program’s written analysis, podcasts, trackers, commissioned papers, and visiting fellows; works closely with Lawfare’s network of experts; and has opportunities to write articles and host podcasts.
Salary is competitive and commensurate with experience within a range of $48,800 \- $60,000\.Benefits include health and dental care (with premiums fully paid by Lawfare), flexible hours, and participation in Lawfare’s 401(k) plan with up to 4% employer match.
What you'll do
The associate editor works at the direction of the managing editor and in close coordination with the research director and editorial team to perform or assist with significant aspects of Lawfare’s work, including:
- Reviewing submissions for possible publication on the Lawfare website, including reviewing AI\-related submissions for substantive accuracy and analytical rigor;
- Soliciting submissions from contributors based on current developments and perceived gaps in Lawfare’s AI and national security coverage;
- Processing accepted submissions to prepare them for publication;
- Working with our podcast team to develop new audio content and daily podcasts, including supporting production of the Scaling Laws podcast—guest coordination, prep research, show notes, and social clips;
- Contributing to long\- and short\-term research projects at the direction of the senior editorial staff, including supporting the program’s commissioned papers and visiting fellows;
- Maintaining AI\-related tracking resources, such as AI on the Docket and the Section 230 Tracker;
- Assisting with the logistics of the program’s workshops and briefings;
- Ensuring final copyediting and posting of publications to the website;
- Maintaining the Lawfare publication schedule;
- Contributing as needed to Lawfare podcasts and articles on topics of particular expertise or need;
- Managing the website, including rotating highlighted pieces, posting relevant documents and livestreams, and controlling quality, in coordination with the web hosting team;
- Scheduling and other administrative responsibilities;
- Assisting with fundraising and donor\-required documentation, as needed;
- Managing and hiring Lawfare interns;
- Completing other duties as assigned by Lawfare management.
Qualifications
This role is an entry\-level position, well\-suited for recent college graduates or those with a year or two of work experience. Candidates will have a bachelor’s degree, preferably in a field related to political science, government, journalism, history, law, or a technology\-related discipline. Outstanding writing, analytical, and research skills are required, as is an interest in artificial intelligence, technology policy, and national security issues. Familiarity with AI and technology\-policy debates is preferred, and the ability to engage credibly with AI capabilities and their legal and policy implications is a plus. Candidates must be able to work effectively with minimal supervision, have excellent interpersonal, verbal, and organizational skills, and have the ability to take initiative and work in a fast\-paced environment. Previous experience in editing, journalism, the legal industry, or digital media is a plus.
*Lawfare* is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
How to Apply
Use the application link to submit the following documents:
- a resume that shows the experience and education requested above;
- a cover letter of no more than two pages that explains how you plan to apply your skills and experiences to *Lawfare*;
- a brief writing sample of no more than 1500 words;
- two references, with email addresses, of people directly familiar with your work.
Questions can be directed to [email protected].
The pay range for this role is:
48,800 \- 60,000 USD per year(Remote (United States))
Salary Context
This $48K-$60K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rippling, 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($54K) sits 75% below the category median. Disclosed range: $48K to $60K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rippling AI Hiring
Rippling has 19 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer, Data Engineer. Positions span Remote, US, New York, NY, US, San Francisco, CA, US. Compensation range: $60K - $330K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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