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Join the future of news
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We’re on a mission to deliver riveting storytelling for all of America. At The Washington Post, you’ll help reinvent news. Our work is driven by a deep investigative spirit and enhanced by innovation to bring audiences closer to the stories that matter most.
About Our Team
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The Washington Post is powered by the passion and talent of our people. It takes all of us to reinvent news. Beyond our award\-winning Newsroom and Opinions teams, we work across many departments, including Brand \& Events, Communications, Customer Care, Engineering \& Product, Finance, Human Resources, Legal, Marketing \& Advertising, Print Operations, and Sales.
Why This Role Matters
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The Washington Post is looking for a Chief AI Officer, reporting to the CTO, to set and drive the AI strategy for the entire company. This is a rare mandate. It spans the newsroom, engineering, advertising, and subscriptions, and it treats AI not as a cost\-cutting tool but as a way to build the products that will define the next era of news.
We have been building in this space for years. Ask The Post AI answers reader questions using only our published reporting. Climate Answers and Conversations extend that approach into new formats. On the commercial side, tools like DealXP put AI directly into the hands of our sales teams. The Chief AI Officer will unify these efforts under one strategy, raise the technical bar across all of them, and decide where we go next.
This person owns the AI roadmap for the company and builds it in genuine partnership with the leaders who run each business. They translate dense technical possibility into value the business can measure, and they hold the line on the thing that matters most to a news organization: the truth. Products that ship under this leader are accurate, grounded, and trustworthy. A reader\-facing feature that hallucinates is not a shipping candidate.
What Motivates You
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- You are driven to shape how AI transforms journalism, engineering, advertising, and subscriptions through products that create measurable business value.
- You are passionate about building reader\-facing AI products that expand what journalism can do rather than focusing solely on operational efficiency.
- You are committed to building AI capabilities thoughtfully, making deliberate decisions about when to build, fine\-tune, or leverage third\-party models.
- You believe accuracy, truth, and trust are non\-negotiable and treat factual errors as unacceptable.
- You enjoy translating complex technical concepts into business value and influencing leaders across editorial, engineering, and commercial organizations.
- You are motivated to build high\-performing AI teams while fostering AI fluency across the organization.
- You are excited to define responsible AI governance, standards, and best practices for a leading news organization.
How You'll Support the Mission
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- Own the company\-wide AI strategy and roadmap. Define where AI creates the most value across journalism, engineering, advertising, and subscriptions, sequence the work, and hold the roadmap accountable to real outcomes alongside the business leaders who own each area.
- Ship reader\-facing products, not just internal efficiency. Lead the design and delivery of AI products that readers, advertisers, and subscribers touch directly, creating new products that grow the business and expand what journalism can do.
- Build, do not just buy. Define the company's approach to leveraging third\-party foundation models, fine\-tuning, retrieval infrastructure, and proprietary capabilities while making deliberate decisions across cost, control, latency, data rights, and defensibility.
- Guarantee accuracy and eliminate hallucination in what we ship. Establish the evaluation frameworks, retrieval grounding, human review, and guardrails that make AI products dependable, define a clear standard for what is publishable, and uphold accountability for the accuracy and trustworthiness of everything we ship.
- Partner across the whole company. Work directly with newsroom leadership, product, engineering, commercial, subscription, standards, and legal teams to earn the trust of the newsroom while delivering AI tools that create measurable value and help commercial teams move revenue.
- Parse dense technical requirements into business value. Move fluently between model architecture discussions and P\&L conversations, separating meaningful technical advances from hype and translating emerging AI capabilities into measurable business value.
- Set the standards, governance, and red lines. Define responsible AI governance, data rights, bias mitigation, risk management, and organizational standards in partnership with newsroom and standards leaders.
- Build the team and the culture. Recruit, develop, and lead world\-class AI talent while fostering AI fluency and responsible AI adoption throughout the organization.
- Establish a unified AI roadmap and evaluation practice that the company trusts, balancing build\-versus\-buy decisions while delivering reader\-facing and commercial AI products that meet The Washington Post's standards for quality, accuracy, and long\-term business impact.
The Skills and Experience You Bring
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- A strong track record of shipping AI products that reached real users and worked, with accuracy and reliability you can speak to concretely. Public\-facing products that did not hallucinate their way into retractions.
- Deep, hands\-on generative AI experience. You understand how modern language models, retrieval systems, fine\-tuning, and evaluation actually work, and you can engage with the technical detail rather than manage from a distance.
- The ability to read dense technical requirements and research and translate them into product and business value.
- Experience building AI capability in\-house, not only assembling vendor tools. You know when each approach is right.
- Executive\-level judgment and the ability to lead through influence across editorial, engineering, and commercial teams with very different cultures and incentives.
- A genuine commitment to accuracy and truth, and the instinct to treat a factual error as unacceptable rather than as an acceptable model limitation.
- Strong communication skills, including the ability to explain hard technical tradeoffs to non\-technical leaders and to earn the trust of a skeptical newsroom.
- Experience in media, publishing, or another domain where trust and accuracy are the core of the product.
- A background that spans research and applied product work.
- Experience standing up AI governance and evaluation practices from scratch.
Collaboration makes us stronger. That’s why our offices are designed with open layouts, modern technology, and easy access to transportation. With certain exceptions for newsgathering and business travel, we work on\-site five days a week.
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Compensation and Benefits
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Wherever you are in your life or career, The Washington Post offers comprehensive and inclusive benefits for every step of your journey:
- Competitive medical, dental and vision coverage
- Company\-paid pension and 401(k) match
- Three weeks of vacation and up to three weeks of paid sick leave
- Nine paid holidays and two personal days
- 20 weeks paid parental leave for any new parent
- Robust mental health resources
- Backup care and caregiver concierge services
- Gender affirming services
- Pet insurance
- Free Post digital subscription
- Leadership and career development programs
*Benefits may vary based on the job, full\-time or part\-time schedule, location, and collectively bargained status.*
The salary range for this position is:
$281,850 \- $469,750 Annual
The actual salary within this range will depend on individual skills, experience, and qualifications as they relate to specific job requirements. This position may be eligible for a bonus or incentive program, and a member of the Talent Acquisition team will discuss bonus payment terms and conditions during the interview process.
Your story awaits. Apply today!
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Learn more about The Post at careers.washingtonpost.com.
Salary Context
This $281K-$469K range is above the 75th percentile 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 The Washington Post, 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 $214,900 based on 6,420 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($375K) sits 75% above the category median. Disclosed range: $281K to $469K.
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.
The Washington Post AI Hiring
The Washington Post has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $469K - $469K.
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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