Interested in this AI/ML Engineer role at The Hearst Corporation?
Apply Now →About This Role
AI is transforming how organizations plan, prioritize, and deliver complex work. The projects driving this transformation need a new kind of project manager—one who understands AI capabilities, can navigate technical ambiguity, and keeps cross\-functional teams aligned around outcomes rather than outputs.
At Hearst, we are hiring an AI Project Manager to drive the delivery of AI\-powered systems across our business units. You will work alongside AI Systems Builders, product leaders, and business stakeholders to scope projects, manage delivery, remove blockers, and ensure that AI initiatives translate into measurable business impact.
This is not a traditional project management role centered on waterfall timelines and status reports. You will operate in fast\-moving, prototype\-driven environments where priorities shift, requirements emerge through experimentation, and success is measured by adoption and outcomes—not task completion.
If you thrive in ambiguity, love keeping builders unblocked, and care more about impact than process, this role may be a great fit.
What You’ll Drive
AI Project Managers oversee a portfolio of AI initiatives across Hearst businesses. Examples of projects you might manage include:
- AI\-powered research and knowledge workflow deployments
- Automation systems that streamline operational processes across business units
- Cross\-functional AI tool rollouts requiring stakeholder alignment and coordination
- Internal AI assistant integrations with enterprise systems
- Evaluation and quality frameworks for AI system reliability
- Multi\-team initiatives combining AI builders, product managers, and business stakeholders
Many projects begin as rapid prototypes and evolve into durable internal products—your job is to shepherd that journey.
How You’ll Work
AI Project Managers operate differently from traditional PMs. You will:
- Partner with AI Systems Builders to scope, plan, and deliver AI\-powered systems
- Manage project portfolios using lightweight, iterative processes suited to AI development
- Translate business needs into clear project briefs, success criteria, and delivery milestones
- Facilitate rapid feedback loops between builders, stakeholders, and end users
- Identify and remove blockers that slow delivery or adoption
Your job is to ensure the team moves efficiently from: problem scoping prototype validated system real business impact
Responsibilities
- Own the end\-to\-end delivery of AI projects from scoping through deployment and adoption.
- Build and maintain project roadmaps, timelines, and dependency maps for AI initiatives.
- Coordinate across AI builders, product teams, and business stakeholders to align priorities.
- Facilitate stakeholder discovery sessions to define problems, requirements, and success criteria.
- Run lightweight delivery cadences adapted to AI development workflows.
- Track and communicate project status, risks, and outcomes to leadership.
- Maintain project documentation and work with knowledge architects on knowledge sharing practices.
Who We’re Looking For
We are looking for experienced project managers with strong technical fluency and the ability to drive outcomes in fast\-moving, ambiguous environments.
Strong Candidate Backgrounds
- Technical project or program managers in software or technology teams
- Product managers with delivery\-heavy experience
- Startup operators or chiefs of staff with technical project oversight
- Delivery leads in engineering or technology organizations
You Likely Have
- 5\+ years managing technical projects or programs in software, AI, or product environments
- Strong understanding of AI development workflows, prototyping cycles, and iterative delivery
- Experience working directly with engineers or AI builders in generative AI environments
- The ability to translate ambiguous business problems into structured project plans
- Comfort operating in fast\-moving environments where requirements evolve through experimentation
What Makes Someone Successful
The strongest AI Project Managers:
- Focus on outcomes and adoption, not just on\-time delivery
- Keep builders unblocked and stakeholders aligned without introducing unnecessary process
- Understand enough about AI systems to ask the right questions and spot risks early
- Communicate clearly across technical and non\-technical audiences
- Care about measurable business impact, not just project milestones
- *They enjoy making teams more effective and ensuring great work actually ships.*
Why This Role Is Different
AI changes the nature of project management.
Traditional PM roles focus on predictable timelines, fixed requirements, and linear delivery. AI projects are different—they involve rapid prototyping, emergent requirements, and outcomes that depend on adoption, not just deployment.
At Hearst, the AI Project Manager operates as a high\-leverage delivery leader who combines project management discipline, technical fluency, and stakeholder engagement to ensure that AI initiatives create real transformation across the company.
*In accordance with applicable law, Hearst is required to include a reasonable estimate of the compensation for this role if hired in New York City. The reasonable estimate, if hired in New York City, is $160,000 \- $175,000\. Please note this information is specific to those hired in New York City. If this role is open to candidates outside of New York City, the salary range would be aligned to that specific location. A final decision on the successful candidate’s starting salary will be based on a number of permissible, non\-discriminatory factors, including but not limited to skills and experience, training, certifications, and education. Hearst provides a competitive benefits package, including medical, dental, vision, disability and life insurance, 401(k), paid holidays and paid time off, employee assistance programs, and more.Strong Candidate Backgrounds*
Hearst is a leading global, diversified information, services, and media company dedicated to innovating, informing audiences and leading with purpose, integrity and a culture of care.
Our portfolio includes more than 360 businesses worldwide. On the consumer side, we operate 35 television stations, 28 daily newspapers and publish more than 200 magazine editions featuring many of the most iconic brands in media. We also hold ownership stakes in leading cable networks such as A\&E, HISTORY, Lifetime and ESPN. On the business\-to\-business side, our companies include Fitch Group, a global leader in financial information and analytics; Hearst Health, which provides intelligence and software that improve care outcomes; and Hearst Transportation, which delivers data and software for aviation, automotive and trucking.
Our strength lies in our people. We value the diverse perspectives that move us forward. We are an Equal Opportunity Employer and makes employment decisions without regard to race, color, religion, national origin, sex or gender, sexual orientation, gender identity, gender expression, age, disability, military or veteran status or any other status protected by federal, state, or local law. We also provide reasonable accommodations to applicants and employees consistent with applicable law.
Degree Level :
Salary Context
This $160K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At The Hearst Corporation, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($167K) sits 8% below the category median. Disclosed range: $160K to $175K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Hearst Corporation AI Hiring
The Hearst Corporation has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Houston, TX, US, New York, NY, US. Compensation range: $175K - $175K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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