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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, Applied AI \& Strategy
We are seeking a Director, Applied AI \& Strategy who views "doing more with less" as a creative challenge, not a budget constraint. In this role, you will be the architect of our internal productivity revolution. Partnering across business functions as well as within Technology \& Operations, you will analyze medium\-to\-highly complex workflows, identify opportunities to apply agentic AI, design those agentic interventions, and help implement those, thus freeing up our people for higher\-value work.
This is a "boots\-on\-the\-ground" leadership role for someone that has demonstrated the ability to build production\-grade AI solutions using LLMs and agents. Ideally, you are also someone that has experience with the modern concept of harness engineering, using popular coding agents for orchestrated software development. Additionally, you should be external\-focused and have your finger on the pulse of AI advancements that are relevant to our business.
MORE ABOUT WHAT YOU'LL DO:
Hands\-on AI Orchestration
- Rapid Prototyping: Rapidly prototype and demonstrate agentic solutions that can make existing workflows more efficient. Based on business feedback, you will refine those prototypes to align with what needs to be built for production. You will help provide cost estimates required to build the business case and make a decision..
- Agent Development: Help build and deploy production grade AI agents using various AI tools, incorporating guardrails, evaluations, and observability to ensure accuracy, reliability, and safety.
- Agent Integration: Integrate AI agents with enterprise resources through MCP. Integrate enterprise applications, including legacy, with agents through APIs.
- Partner with Legal \& Business Affairs (L\&BA) and Cyber Security to ensure AI adoption respects guardrails and governance.
- Define and track Productivity \& ROI measuring hours reclaimed, cost savings and increase in efficiency.
- Maintain composability in toolsets to ensure future agility.
Industry Insights
- Stay at the forefront of AI developments specifically relevant to the Media Industry and update leadership on findings.
- Move beyond updates to provide leadership with actionable “Buy, Build, Ignore” frameworks based on the relevant AI tools that solve friction points for media \& entertainment.
Administration
- Translate high\-level leadership vision into a tactical technical roadmap, prioritizing workstream that yield the highest ROI.
- Rigorously manage scope and stakeholder expectations to move from Proof\-of\-Concept to Production\-Ready.
- Perform technical and functional audits of emerging AI tools that align with media industry workflows.
- Create sensible budgets in high\-ambiguity environments.
BASIC REQUIREMENTS:
- Bachelor’s degree or demonstrated practical experience.
- 8\+ Years of total experience in a technical function (i.e. Solutions Architecture, Digital Transformation, or Technical Operations) with a proven track record of delivering enterprise\-grade solutions and advanced workflow integration including demonstrated experience in systems integration and workflow automation.
- Expert in architecting logic across complex SaaS ecosystems, not just automation.
- 2\+ years of hands\-on experience building with LLMs for production ready agents. This must include demonstrated proficiency in advanced prompting techniques, multi\-agent orchestration, RAG Frameworks and a solid grasp of unstructured content and techniques for curation, enrichment, and accurate retrieval for injecting context into LLMs.
- 3\+ years of experience managing complex stakeholder relationships, with a proven ability to translate high\-level business strategy into tactical execution plans
- Rapid Prototyping: Demonstrated ability to act as a “Vibe Coder”, utilizing AI\-augmented development tools (i.e. Cursor, Replit, Claude, etc) to ship functional code and automations at high velocity.
- Hands\-on experience implementing RAG architectures at scale and/or fine\-tuning large language models.
- Certifications in AI/ML technologies and cloud platforms will be an added advantage
- Background in statistical modeling or mathematics 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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