Interested in this AI/ML Engineer role at News Corp?
Apply Now →About This Role
About the Team:
The Trust at The Wall Street Journal
About the Role
This Creative Director leads end\-to\-end creative development for The Trust at The Wall Street Journal, driving work from concept through execution. The role focuses on integrating cutting\-edge AI capabilities into premium content and real\-world brand experiences, while also expanding our live content programs and events. It requires 12\+ years of creative leadership experience and a proven ability to turn bold ideas into polished, high\-impact work across platforms.
You Will:
- Own and articulate the creative vision for AI\-informed content and experiential programs from strategy through live execution.
- Direct the overarching creative strategy and execution, leveraging AI tools and experiences to drive innovation, efficiency, and high\-quality design.
- Anticipate issues and develop tailored solutions to solve complex creative challenges, ensuring projects are delivered on time and adhere to brand standards.
- Communicate with diverse stakeholders and influence decisions by partnering with cross\-functional teams to seamlessly integrate AI technologies into the creative process.
- Lead cross\-functional teams across editorial, product, design, and client services to develop innovative formats, immersive storytelling, and interactive brand moments.
- Integrate AI\-assisted workflows (prompting, rapid prototyping, generative tools) into the creative process to enhance speed, insight, and originality.
- Ensure all work reflects the premium standards, cultural authority, and brand integrity of WSJ.
- Present concepts confidently to senior stakeholders and clients, translating complex ideas into compelling narratives.
- Oversee production and experiential delivery, maintaining high craft and measurable business impact.
You Have:
- 12\+ years of creative direction experience across experiential, brand, or integrated marketing environments.
- Demonstrated leadership of multidisciplinary teams and large\-scale experiential initiatives.
- Fluency in AI\-enabled creative tools and workflows (e.g., generative content, rapid prototyping, data\-informed personalization).
- Strong strategic thinking paired with exceptional storytelling and presentation skills.
- Portfolio showcasing premium craft, cultural fluency, and results\-driven campaigns.
- Ability to operate at both conceptual and executional levels in a fast\-paced, collaborative environment.
Our Benefits
- Comprehensive Healthcare Plans
- Paid Time Off
- Retirement Plans
- Comprehensive Medical, Dental and Vision Insurance Plans
- Education Benefits
- Paid Maternity and Paternity Leave
- Family Care Benefits
- Commuter Transit Program
- Subscription Discounts
- Employee Referral Program
Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status or any other protected characteristic under applicable law. EEO/Disabled/Vets
Reasonable Accommodation
We are committed to providing reasonable accommodation for qualified individuals with disabilities in our job application and/or interview process. If you need assistance or accommodation in completing your application or participating in an interview due to a disability, email us at talentresourceteam@dowjones.com. Please put "Reasonable Accommodation" in the subject line and provide a brief description of the type of assistance you need. This inbox will not be monitored for application status updates.
Please refer to the privacy notice at the bottom of this page for submitting any data access, deletion, or other data subject rights requests, where permitted under your local laws and regulations.
Business Area: Dow Jones \- Media Sales
Job Category: Editorial/Journalism
Union Status:
Non\-Union / A clear and likely internal candidate
Base Pay Range: $165,000 \- $205,000
We’re committed to offering competitive and flexible compensation to attract top talent. This pay range reflects our good faith estimate for the role and may vary based on a candidate’s experience, skills, location, and other relevant factors.
For bonus\-eligible roles, targets are determined based on multiple considerations, including market benchmarks and individual contributions.
For benefits\-eligible roles, we offer a comprehensive and competitive benefits package covering health, retirement, wellbeing, and more, along with optional benefits to meet the diverse needs of our employees.
Dow Jones is a global provider of news and business information, delivering content to consumers and organizations around the world across multiple formats, including print, digital, mobile and live events. Dow Jones has produced unrivaled quality content for more than 130 years and today has one of the world’s largest newsgathering operations globally.
It is home to leading publications and products including the flagship Wall Street Journal, America’s largest newspaper by paid circulation; Barron’s, MarketWatch, Mansion Global, Financial News, Dow Jones Risk \& Compliance and Dow Jones Newswires. Dow Jones is a division of News Corp (Nasdaq: NWS, NWSA; ASX: NWS, NWSLV).
Req ID: 54217
Salary Context
This $165K-$205K range is above the median 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 News Corp, 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($185K) sits 15% below the category median. Disclosed range: $165K to $205K.
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.
News Corp AI Hiring
News Corp has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager. Positions span New York, NY, US, Austin, TX, US. Compensation range: $95K - $270K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.