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About This Role
iHeartMedia
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The audio revolution is here – and iHeart is leading it! iHeartMedia, the number one audio company in America, reaches 90% of Americans every month \- a monthly audience that’s twice the size of any other audio company – almost three times the size of the largest TV network – and almost 4 times the size of the largest ad\-supported music streaming service. In fact, we have:
- More \#1 rated markets than the next two largest radio companies combined;
- We’re the largest podcast publisher, with more monthly downloads than the second\- and third\-largest podcast publishers combined. Podcasting, the fastest\-growing new media, today has more monthly users than streaming music services or Netflix;
- iHeart is the home of many of the country’s most popular and trusted on\-air personalities and podcast influencers, who build important connections with hundreds of communities across America;
- We create and produce some of the most popular and well\-known branded live music events in America, including the iHeartRadio Music Festival, the iHeartRadio Music Awards, the iHeartCountry Festival, iHeartRadio Fiesta Latina and the iHeartRadio Jingle Ball Tour;
- iHeartRadio is the \#1 streaming radio digital service in America;
- Our social media footprint is 7 times larger than the next largest audio service; and
- We have the only complete audio ad technology stack in the industry for all forms of audio, from on demand to broadcast radio, digital streaming radio and podcasting, which bring data, targeting and attribution to all forms of audio at an unparalleled scale. As a result, we’re able to combine our strong leadership position in audience reach, usage and ad tech with powerful tools and insights for our sales organizations to help them build success for their clients at a more efficient cost than any other option.
Because we reach almost every community in America, we’re committed to providing a range of programming that reflects the diversity of the many communities we serve – and our company reflects that same kind of diversity. Our company values stress collaboration, curiosity, welcoming dissent, accepting mistakes in the pursuit of new ideas, and respect for everyone.
Only one company in America has the \#1 position in everything audio: iHeartMedia!
If you’re excited about this role but don’t feel your experience aligns perfectly with the job description, we encourage you to apply anyway. At iHeartMedia we are dedicated to building a diverse, inclusive, and authentic workplace and are looking for teammates passionate about what we do!
What We Need:
The Director of AI Enablement is responsible for defining and executing the company's AI product vision across its portfolio of SaaS and on\-premise software solutions. This leader identifies opportunities to leverage Generative AI, Agentic AI, Model Context Protocol (MCP), Large Language Models (LLMs), and emerging AI technologies to create differentiated customer value and accelerate business growth.
Working across Product Management, Engineering, Customer Success, Sales, and Executive Leadership, this role develops AI strategy, prioritizes investments, and drives adoption of AI capabilities that improve customer workflows, automate operations, and unlock new revenue opportunities.
This position is both strategic and highly technical, requiring a deep understanding of modern AI architectures, enterprise software, APIs, orchestration frameworks, and product management.
What You'll Do:
### AI Product Strategy
- Develop and own the company's AI product roadmap.
- Identify opportunities to embed AI across existing products and new offerings.
- Evaluate emerging AI t
- echnologies and recommend strategic investments.
- Define AI product principles, governance, and long\-term platform direction.
- Create business cases for AI initiatives, including ROI, pricing, and commercialization strategies.
- ### Agentic AI \& Automation
- Define strategy for agent\-based workflows and autonomous business processes.
- Identify use cases where AI agents can automate customer and internal workflows.
- Establish standards for agent orchestration, memory, planning, and tool usage.
- Evaluate commercial and open\-source agent frameworks.
- ### AI Integrations \& MCP Strategy
- Define the company's approach to AI interoperability using standards such as Model Context Protocol (MCP).
- Identify opportunities for AI integrations with customers' enterprise systems.
- Partner with engineering to build reusable AI connectors and integration frameworks.
- Establish strategies for exposing company products as AI\-capable services and tools.
- ### Product Leadership
- Partner with Product Managers to prioritize AI capabilities across the portfolio.
- Translate customer problems into AI\-enabled product features.
- Balance innovation with practical customer value and product reliability.
- Develop multi\-year AI product roadmaps aligned with corporate strategy.
- ### Cross\-Functional Leadership
- Work closely with Engineering to define technical direction and architecture.
- Partner with Sales and Marketing on AI positioning and go\-to\-market strategy.
- Support Customer Success in identifying AI adoption opportunities.
- Present AI strategy and roadmap to executive leadership and customers.
What You'll Need:
8\+ years in Product Management, Product Strategy, or Technical Product Leadership. The emphasis that this is a practical, product focused position cannot be understated.
Experience delivering enterprise software products.
Strong understanding of modern AI technologies including:
- Large Language Models (LLMs)
- Retrieval\-Augmented Generation (RAG)
- Agentic AI
- Prompt engineering
- Vector databases
- AI orchestration frameworks
- Experience with Model Context Protocol (MCP).
- Experience with AI platforms such as OpenAI, Anthropic, Google Gemini, Azure AI, or AWS Bedrock.
Experience working with APIs, SDKs, cloud platforms, and integration technologies.
Strong understanding of software architecture and modern development practices.
Excellent executive communication and strategic planning skills.
Demonstrated ability to influence cross\-functional teams without direct authority.
Preferred
-------------
- Familiarity with LangChain, Semantic Kernel, AutoGen, CrewAI, or similar orchestration frameworks.
- Experience with enterprise integration technologies and workflow automation.
- Experience building platform capabilities used across multiple products.
- MBA or advanced technical degree is a plus.
What You'll Bring:
- Respect for others and a strong belief that others should do this in return
- Demonstrated initiative and achievement\-oriented leadership
- Ability to manage several projects at a time
- Growth mindset and desire for continued knowledge sharing and learning
- Understanding of impact of your own decisions and decisions of your team
- Strong business insights that contribute to resolving complex problems
- Catalyst for new and innovative ideas
- Ability to identify and support new opportunities for continued improvement across business
- Ability to interact with individuals of all levels and maintain professional relationships
- Strong relationships with other leaders with the ability to manage external business partners where appropriate
Location:
Austin, TX: 5001 Plaza On the Lake, Suite 105, 78746
Position Type:
Regular
Time Type:
Full time
Pay Type:
Salaried
Benefits:
iHeartMedia’s benefits offering is flexible and offers a variety of choices to meet the diverse needs of our changing workforce, including the following:
- Employer sponsored medical, dental and vision with a variety of coverage options
- Company provided and supplemental life insurance
- Paid vacation and sick time
- Paid company holidays
- A Spirit day to encourage and allow our employees to more easily volunteer in their community
- A 401K plan
- Employee Assistance Program (EAP) at no cost – services include telephonic counseling sessions, consultation on legal and financial matters, emotional well\-being, family and caregiving
- A range of additional voluntary programs, such as spending accounts, student loan refinancing, accident insurance and more!
We are accepting applications for this role on an ongoing basis.
The Company is an equal opportunity employer and will not tolerate discrimination in employment on the basis of race, color, age, sex, sexual orientation, gender identity or expression, religion, disability, ethnicity, national origin, marital status, protected veteran status, genetic information, or any other legally protected classification or status.
Non\-Compete will be required for certain positions and as allowed by law.
Our organization participates in E\-Verify. Click here to learn about E\-Verify.
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 iHeartMedia, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
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.
iHeartMedia AI Hiring
iHeartMedia has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 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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