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About This Role
*Work Locations:* *With the exception of some select roles that have in-office requirements, A+E Global Media operates on a flexible model that allows for remote, hybrid or full time in office work (in certain locales).*
*Office locations include New York City, Los Angeles, Chicago, and Stamford, CT.*
*Our list of eligible states in which employees may work remotely* *includes: California,* *Connecticut, Florida, Georgia, Illinois, Indiana, Maryland, Massachusetts, Michigan, Minnesota, Nevada, New Hampshire, New Jersey, New York, North Carolina, Oregon, South Carolina, South Dakota, Texas, West Virginia, Wisconsin, and Wyoming.*
Division Story
A+E’s Technology team is deep-rooted in the heart of our business. We have great people and great technologies, and together we take on the toughest challenges. As innovators, we choose to iterate, pivot, and adapt quickly. We’ve reinvented the way A+E leverages technology to produce and sell world-class content. We’ve modernized our core solutions and embraced a cloud first approach. Perched on the virtues of our “Technology Code”, we make technology better, create solutions together, and most of all, we have fun with it. Our team members are motivated individuals who help each other do remarkable things every day. Together we deliver best-in-class solutions that transform the way A+E works. If this sounds like something you want to be a part of, we want to hear from you!
Job Description
THE ROLE: Senior Director, Engineering - Applied AI
A+E Global Media is seeking a strategic and hands-on Senior Director, Engineering - Applied AI to lead the design, development, and delivery of AI-driven capabilities across our technology platforms. Reporting directly to the VP of Engineering, you will play a critical role in scaling our applied AI and generative AI initiatives while collaborating with product, data, and business teams to bring next-generation media experiences to life.
You will serve as a central technical leader, coordinating across multiple teams — including Data Engineering, Data Science, Product Management, Software Engineering, and Creative AI — to drive innovation, ensure delivery excellence, and align AI capabilities with business goals.
This is a unique opportunity to found and shape A+E Global Media’s Applied AI engineering function from the ground up. You’ll be the first dedicated Applied AI leader on the team, with the autonomy to define the vision, build a team of consultants, and lay the technical and cultural foundation for our Applied AI strategy moving forward.
MORE ABOUT WHAT YOU'LL DO: Senior Director, Engineering - Applied AI
Key Responsibilities
Applied AI Strategy & Execution
- Drive the technical roadmap for applied AI across key product and business areas, including content intelligence, personalization, automation, and creative tooling.
- Partner with the VP of Engineering and peers to define how AI integrates into A+E Global Media’s broader technology and product vision.
- Evaluate and implement cutting-edge AI techniques, including GenAI, LLMs, vision models, and recommendation systems.
- Evaluate emerging AI technologies, frameworks, and vendors to ensure the team remains at the forefront of innovation and leverages the best available tools for impact and scale.
- Drive development of custom AI solutions using Retrieval-Augmented Generation (RAG) pipelines and model fine-tuning to meet business-specific needs across media, creative workflows, and internal business operations, including defining evals and benchmarks to ensure performance, grounding, and reliability.
- Design and lead systems that extract value from large, unstructured content sets — including PDFs, SharePoint documents, and multimedia assets (video, audio, image) — leveraging a combination of NLP, computer vision, multimodal models, LLMs, RAG, RLHF, and enterprise-scale retrieval techniques.
- Drive the thoughtful adoption of AI-powered tools within engineering to enhance developer productivity, model experimentation, and operational efficiency.
Team Leadership
- Build the Applied AI engineering function from scratch — defining the team’s structure, hiring its first members, and establishing best practices and a strong technical culture from day one.
- Lead and mentor a growing team of engineers focused on applied AI systems, including full-stack AI, MLOps, and platform integrations.
- Cultivate a culture of experimentation, engineering rigor, and inclusive collaboration.
- Attract and retain top talent while supporting ongoing professional development.
Delivery & Cross-Functional Collaboration
- Oversee the full lifecycle of applied AI systems, including model integration and orchestration — from prototyping and data exploration to deployment, monitoring, and retraining.
- Collaborate with Data Engineering and Data Science teams to ensure robust data pipelines and experimentation workflows.
- Partner with Product teams to co-develop AI-enabled features and prioritize high-impact initiatives.
- Work closely with other internal A+E Engineering teams to embed applied AI into production systems.
- Serve as a key technical counterpart to the Creative Generative AI teams, enabling media and video generation capabilities.
Operational Excellence
- Ensure engineering excellence across performance, scalability, monitoring, and AI system reliability and governance.
- Build and maintain scalable, cloud-based AI infrastructure.
- Champion responsible AI development aligned with privacy, IP, and ethical standards.
BASIC REQUIREMENTS: Senior Director, Engineering - Applied AI
- 12+ years of software engineering experience including team leadership experience
- Significant recent focus on application of AI technologies including experience applying LLM models or systems to real-world products and workflows.
- Proven track record of building and scaling engineering teams in a production AI environment.
- Experience working with Embeddings, RAG, RLFH, Fine Tuning etc at enterprise scale
- Experience with Python and/or Typescript/Javascript
- Experience designing or applying evals LLM models (LLMs), including prompt testing, grounding, hallucination detection, and performance benchmarking.
- Excellent communication skills and experience working on cross-functional initiatives.
Preferred
- Experience working with large-scale data in Adtech or Advertising-related platforms is a plus.
- Experience with Snowflake, Databricks, or similar systems is a plus.
- Experience with AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and deploying models to cloud environments is a plus
- Experience with data pipelines and integration patterns is a plus including MLOps
- Hands-on experience implementing RAG architectures at scale and/or fine-tuning large language models.
- Experience with diffusion/image/video models is a nice to have
- Exposure to creative pipelines or AI-enhanced content workflows is a plus
- Background in statistical modeling or mathematics is a plus.
- Familiarity with data privacy, copyright, and ethical issues in applied AI applications is helpful but not needed
- Experience in the media, entertainment, or content-tech industry is a plus.
Compensation
Annual Pay Range: $220,513 - $258,000Annual Incentive Target: 20.00%
*The annual/hourly**pay range displayed serves as a* *good faith estimate of* *the*
*minimum and* *maximum* *base* *pay* *range* *for this role.* *Compensation for the role* *will*
*be based on* *a* *number of different* *factors such as* *a candidate’s qualifications, skills,*
*competencies,* *location, and* *experience.* *A+E offers a competitive total compensation*
*package, which* *includes healthcare coverage, 401k matching, and a range of other benefits. Learn more at* *www.aegm.com/careers.*
*A+E Global Media proudly provides equal employment opportunity for all employees and job applicants, and makes employment decisions consistent with this principle. The company’s employment actions and decisions – including recruitment, hiring, training, promotion, demotion, compensation, transfer, layoff, and termination – are made without regard to an employee’s race, color, religion, creed, age, national origin, ancestry, sex (which includes pregnancy, childbirth, breastfeeding, and related medical conditions), gender, sexual orientation, gender identity, gender expression, marital status, alienage or citizenship status, physical and/or mental disability, medical condition, family and medical leave status, genetic information, military or veteran status, or any other characteristic protected by applicable law.*
*A+E Global Media is a joint venture of the Hearst Corporation and The Walt Disney Company.*
*We are proud to be an Affirmative Action/Equal Opportunity* *Employer/Disabled/Veterans.*
Salary Context
This $220K-$258K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Hearst Networks EMEA, 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. This role's midpoint ($239K) sits 55% above the category median. Disclosed range: $220K to $258K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Hearst Networks EMEA AI Hiring
Hearst Networks EMEA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $258K - $258K.
Location Context
AI roles in New York pay a median of $204,100 across 1,633 tracked positions. That's 7% above the national 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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