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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
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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
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THE ROLE: Director of Engineering, Applied AI
A\+E Global Media is seeking a strategic and hands\-on Director of Engineering, Applied AI to lead the design, development, and delivery of AI\-driven capabilities across our technology platforms. Reporting directly to the VP, Corporate Application Engineering \& Enterprise Data, 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 technical leader, coordinating with multiple teams — including Data Engineering, Application Development \& Integrations — to drive innovation, ensure delivery excellence, and align AI capabilities with business goals.
This is a unique opportunity to shape A\+E Global Media’s Applied AI engineering function. As an Applied AI Engineering leader on the team, you’ll build and nurture a team of AI Engineers and improve the technical foundation for our Applied AI engineering team going forward.
MORE OF WHAT YOU'LL DO:
Applied AI Engineering Leadership
- Drive the technical roadmap for Applied AI across key enterprise applications and business workflows.
- Evaluate and implement cutting\-edge AI techniques, frameworks, and best practices including LLMs to ensure the team remains at the forefront of innovation and leverages the best available tools for media and entertainment use cases.
- Drive development of custom AI solutions using Retrieval\-Augmented Generation (RAG) pipelines and model fine\-tuning to meet business\-specific needs across departments, including defining evaluation criteria and benchmarks to ensure performance, grounding, and reliability.
- Design and lead systems that extract value from large structured and unstructured datasets — including PDFs in S3, SharePoint documents and emails using LLMs, RAG, RLHF, and enterprise\-scale modern AI techniques.
- Build Agentic AI systems that streamline \& automate workflows using LLMs
- Cultivate a culture of experimentation, engineering rigor, and inclusiveness where bold ideas are tested quickly and learned from openly.
Delivery \& Cross\-Functional Collaboration
- Oversee the full lifecycle of Agentic AI applications, from prototyping and data exploration through model integration, orchestration, deployment, monitoring, and retraining.
- Partner with Data Engineering and Data Science teams to design robust, reusable data pipelines and experimentation workflows that accelerate Applied AI initiatives.
- Drive AI\-enabled data accessibility across the organization, delivering solutions and tooling that empower stakeholders at all levels, from analysts to senior executives to extract actionable insights.
- Partner with Product and Business Analysts team to co\-develop AI\-enabled features and prioritize high\-impact initiatives.
- Work closely with Application Engineering teams to embed Agentic AI into production systems, improve user experience, ensuring seamless integration, scalability, and long\-term maintainability.
Operational Excellence
- Drive engineering excellence and delivery cadence across the AI stack — observability, latency, model monitoring, evaluation harnesses, rollback paths, and sprint\-level prioritization across concurrent workstreams.
- Build and maintain scalable, cloud\-based AI infrastructure that supports rapid experimentation, model serving, and cost\-controlled inference at media\-scale workloads.
- Champion responsible AI development aligned with privacy, IP, copyright, and ethical standards — particularly relevant for media content and audience data.
BASIC REQUIREMENTS:
Required
- 8 or more years of technology engineering work experience including 2\+ years in Applied AI software integration experience
- Will have 3 or more years of team leadership experience
Experience with Python and/or Typescript/Javascript. Well\-versed in SQL.
- Experience working with LLM provider APIs (e.g., OpenAI, Anthropic, Google Gemini) and frameworks (e.g., LangChain, LlamaIndex, AutoGen, AI SDK) to build agentic or multi\-agent AI workflows
- Experience in building and deploying AI\-powered applications at scale, with a strong focus on applying large language models (LLMs) to real\-world products and workflows.
- Experience working with Agentic AI, Embeddings, RAG, RLHF and other AI techniques at enterprise scale.
- Experience designing or applying evals on systems built on top of LLMs, including prompt testing, grounding, hallucination detection, and performance benchmarking.
- Excellent communication skills and experience working on cross\-functional initiatives.
Preferred
- Experience with agentic AI harnesses and orchestration frameworks (e.g., LangGraph, Semantic Kernel) for building, managing, and deploying multi\-agent systems is a plus
- Experience working with big data and data warehouse projects is a plus
- Familiarity with vector databases and RAG pipelines (e.g., Pinecone, Weaviate, Milvus, ChromaDB, pgvector) for LLM\-powered applications is a plus
- Experience building full\-stack AI\-powered applications using modern web frameworks (e.g., Next.js, React) to deliver user\-facing AI products and internal tooling is a plus.
- Hands\-on experience with ML/deep learning libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers, Scikit\-learn) and deploying models to production is a plus
- Experience with MLOps tooling (e.g., MLflow, Weights \& Biases, Kubeflow) and cloud ML platforms (e.g., AWS SageMaker, Google Vertex AI, Azure ML) is a plus.
- Familiarity with data engineering tools and platforms (e.g., Databricks, Snowflake, Apache Spark, dbt, Kafka) 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, ad\-tech, mar\-tech or publishing industry is a plus.
Compensation
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Annual Pay Range: $188,034 \- $220,000
Annual Incentive Target: 17\.50% *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 $188K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($204K) sits 14% above the category median. Disclosed range: $188K to $220K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Hearst Networks EMEA AI Hiring
Hearst Networks EMEA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $220K - $220K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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