AI Whistleblowing Policy Lead (Washington, DC)

$130K - $190K Washington, DC, US Senior AI/ML Engineer

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Skills & Technologies

AnthropicOpenai

About This Role

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Deadline: 8 November 2026. We will begin reviewing applications from 5 October 2026 and will continue to consider them on a rolling basis until the role is filled. First-round interviews are expected to take place from mid to late November, with the process anticipated to close by late November / early December***. Timelines are indicative and may change.*

LOCATION: Washington, DC.

Most high-stakes moments in AI over the coming years will involve frontier company insiders having and raising concerns: within companies, to regulators, or through other pathways.

Those insiders tell us they face enormous uncertainty along the “concern raising” path - from detecting concerns to whether and how to voice concerns with maximum impact and minimal risk, which even legally protected whistleblowing can carry.

To date, no organization has dedicated itself deeply to delivering the best strategic, frontier-AI-specific guidance to these individuals, their supporters, and their attorneys (our partners). We exist to ensure that each insider disclosure is maximally impactful and safe for the person making the disclosure.

Working closely and continuously with our partners in AI and whistleblowing policy, you will own AIWI's work to achieve this goal by closing two gaps:

  1. Strengthening the laws that protect AI whistleblowers.
  1. Improving the effectiveness and safety of the channels through which AI whistleblowers are, theoretically, protected to disclose.

This work is focused on U.S. federal and state level.

Tasks

About the role

Your job is to strengthen and inform the legislation and regulator-level whistleblowing channels that determine both the legal protections available to AI whistleblowers and the effectiveness of channels through which they can disclose.

In the US, that means prioritizing and providing input on federal and state whistleblower bills, on AI bills with implications for whistleblowing, and advising agencies, congressional offices, or other reporting bodies as they set up their disclosure channels.

In a given month, you might:

  • Read a newly introduced AI bill and suggest edits to address phrasing that would make non-compliance impossible for an insider to surface.
  • Draft model language for a whistleblower provision and get it into the hands of a staffer who asked for help.
  • Write comments into a shared doc about why we should accept or reject a proposed amendment that might improve a bill’s chances.
  • Rapidly assemble a coalition and write a letter on a specific bill, publish an oped or speak to a journalist on the relevance of a specific provision.
  • Advise an agency setting up a new channel on intake, security, and triage by bringing together and collaborating closely with partners, contractors, fellows.
  • Manage fellows writing on exploratory topics: For instance on how to design a whistleblowing office enforcing international pause agreements.
  • Work with a lobbyist to map out the legislative process for the next six months and identify the core decision-makers you’ll need to inform.

What you will own

Improve Legal protections

Inform US federal and state whistleblower legislation, and the whistleblowing provisions in adjacent AI bills * - through model policy, technical drafting support for congressional and state offices, policymaker briefings, and public education.

  • Provide input on non-whistleblowing AI bills where the language determines whether non-compliance can be detected or disclosed by insiders.
  • Build and orchestrate broad coalitions to strengthen (non-)AI whistleblowing law in key states.
  • Set policy priorities: Development of thought leadership on gaps to be closed and feasibility of addressing those gaps.

Make External Disclosure channels safer and more effective for AI insiders

Best practice for setting up whistleblowing channels largely exists. What matters is reaching out, offering support, and helping offices and agencies adopt them.

  • Work with our partners to support congressional, federal, and state bodies in building and improving their channels, including: Congressional Offices, emerging federal AI institutions, state AGs, and state agencies designated to receive disclosures.
  • Build relationships with those disclosure channels to uncover opportunities for improvements and training/support needs.

Maintain a view of bodies’ channel quality * - useful to insiders, support organizations, and offices themselves.

Network and intelligence

  • Build and maintain relationships with congressional offices, policy advocacy organizations, and the bodies operating disclosure channels.
  • Keep track of the AI and whistleblower bills moving across state legislatures, and pick the few worth AIWI engaging with.

Thought leadership

  • Release publications and op-eds conducive to your policy efforts.
  • Exploratory research, especially in the context of using whistleblowing for verification of international agreements.

Reporting to the founder, you will manage fellows early in your trajectory with us, with a path to managing team members as your function grows.

Your work will primarily be in the civilian domain; on the National Security side, we support our partners rather than drive work ourselves. All of AIWI's policy work is non-partisan and collaborative.

Requirements

Your profile

We expect a successful candidate to bring policy or advocacy experience in either AI or whistleblowing, and to be able to rapidly build understanding of the other.

You should have a strong understanding of how bills are passed and a network that will enable you to do your work well. We usually expect multiple years of relevant experience, but a candidate with a strong record of high-agency work in AI governance may fit with less.

Beyond that, we expect multiple of the following traits:

  • You are driven and relentlessly resourceful. You decide what needs to happen and make it happen, without waiting to be told. You get around obstacles rather than stopping at them, and you are never afraid of getting your hands dirty on the operational work this involves.
  • You learn fast. You can drop into an unfamiliar bill, regulatory process, or technical debate, ask sharp questions, and reach a working grasp within days. You are energized by conversations with fellow researchers.
  • You exhibit exceptionally strong judgment. You can arrive at a well-informed point of view on why particular elements of a whistleblowing policy matter, as well as where AIWI should spend its time to make change.
  • You are a natural networker. You seek people out rather than waiting to be introduced, and build trust quickly — with a congressional staffer, an agency official, or an advocate who sees the world very differently. You come across as smart, earnest, and worth listening to, and can build a network from a standing start — you don't need to arrive with a deep DC network.
  • You are a strong communicator. You can turn a complex point into model policy language, a two-page briefing, or an op-ed, and pitch it as easily to an expert as to a staffer who needs the gist in five minutes.
  • You are energized rather than thrown by tight, unpredictable deadlines. Legislative timing is outside your control; when an opening appears, you move on it fast.
  • You care deeply about AI going well — enough to work hard, through setbacks, to make good policy happen.
  • You understand the weight of this role and display unwavering integrity. Your work will shape whether critical safety concerns can be raised and heard, and what it costs the people who raise them. This should weigh on and motivate you.

Backgrounds likely to be suitable may include:

  • Legislative staffer, policy counsel, or legislative aide in a congressional office or state legislature
  • Policy or government affairs staff at an AI governance, safety, or evaluations organization, with a strong record of high-agency work
  • Policy or advocacy staff at a whistleblower protection or accountability organization
  • Policy or regulatory person at a think-tank or advocacy organization working on emerging technology

Benefits

Package

  • USD 130,000-190,000 commensurate with experience
  • 100% of medical, dental, and vision premiums covered for employees, and 50% for spouses and dependents
  • 401(k) with a 6% employer contribution, no match required
  • 20 days PTO plus federal holidays, and the week between Christmas and New Year's
  • Paid parental leave
  • Professional development budget
  • A home office or equipment stipend for hybrid staff
  • Wellness budget

Why join?

  • Impact: Your work changes the policy conditions that determine whether critical AI concerns can be raised safely and heard by the people best placed to act on them.
  • Intellectually stimulating: Work at the intersection of broader AI policy, whistleblowing policy, and alongside leading thinkers in the field.
  • Independence and ownership: Run AIWI's policy work in consultation with the founder, and shape how this function grows as the organization scales.
  • Growth: Join at a formative moment of AIWI and AI safety.
  • A supportive team: Who will stand by your side.

Who you'd be joining:

AIWI is an independent non-profit launched in late 2024. We are ~4 team members today and are scaling to ~15 within the year, spread across the Bay Area, DC, and London. Joining now, you will significantly shape what the future of AI insider support looks like. We are funded until 2029.

Examples of our work to date:

  • Policy: We pushed for and shaped the EU AI Office's channel, the world's first AI whistleblowing channel. In the US, we support the federal AI Whistleblower Protection Act and have provided expert research input on implementing SB 53's incident reporting mechanisms through a research coalition, which we helped set up. Read our work in Tech Policy Press, the Institute for Law & AI, and Transformer, and see our AI Whistleblowing Law Best Practice Guide.
  • Insider education & support: Our online campaigns have reached thousands of company employees. We produce guides (available upon request), online courses, and help establish connections to our great partner attorneys and support (e.g. on legal advice and defense funding).
  • Advocacy: Anthropic published its whistleblowing policy and OpenAI strengthened theirs following our Publish Your Policies Campaign.
  • Network: We closely collaborate with the world's leading whistleblowing and AI organizations.

Our ambition is to ensure that each insider disclosure is maximally impactful and safe for the person making the disclosure. Joining us means dedicating yourself to achieving that goal with us.

Hiring process:

  • Application review
  • Introduction call with our team
  • Interview with the hiring manager
  • Work task
  • Final interview, including meeting our founder
  • References

Referral program:

We provide a reward of USD ~5,000 for successful referrals upon completion of a 6-month employment period. Allocation will occur based on a successful applicant's attribution in the case of multiple referrers.

How to apply

  • Apply by submitting your application via Join.com.
  • We will begin reviewing applications on a rolling basis from 5 October.
  • The first round is set to take place in mid to late November.
  • Therefore, anticipated close date is early December.

Work authorization

You must be authorized to work in the US. We cannot sponsor visas.

Equal opportunity

AIWI is an equal opportunity employer. We welcome applicants of all backgrounds and do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected status.

Salary Context

This $130K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).

View full AI/ML Engineer salary data →

Role Details

Title AI Whistleblowing Policy Lead (Washington, DC)
Location Washington, DC, US
Category AI/ML Engineer
Experience Senior
Salary $130K - $190K
Remote No

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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At The AI Whistleblower Initiative, 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

Anthropic (2% of roles) Openai (3% of roles)

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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $130K to $190K.

Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.

The AI Whistleblower Initiative AI Hiring

The AI Whistleblower Initiative has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $190K - $190K.

Location Context

Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.

The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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

Based on 13,048 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $211,950. Actual compensation varies by seniority, location, and company stage.
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.
About 6% of the 9,079 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
The AI Whistleblower Initiative is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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