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About This Role
Company Description
NBCUniversal is one of the world's leading media and entertainment companies. We create world\-class content, which we distribute across our portfolio of film, television, and streaming, and bring to life through our global theme park destinations, consumer products, and experiences. We own and operate leading entertainment and news brands, including NBC, NBC News, NBC Sports, Telemundo, NBC Local Stations, Bravo, and Peacock, our premium ad\-supported streaming service. We produce and distribute premier filmed entertainment and programming through our powerhouse film and television studios, including Universal Pictures, DreamWorks Animation, and Focus Features, and the four global television studios under the Universal Studio Group banner, and operate industry\-leading theme parks and experiences around the world through Universal Destinations \& Experiences, including Universal Orlando Resort, home to Universal Epic Universe, and Universal Studios Hollywood. NBCUniversal is a subsidiary of Comcast Corporation. Visit www.nbcuniversal.com for more information.
Our impact is rooted in improving the communities where our employees, customers, and audiences live and work. We have a rich tradition of giving back and ensuring our employees have the opportunity to serve their communities. We champion an inclusive culture and strive to attract and develop a talented workforce to create and deliver a wide range of content reflecting our world.
Job Description
The Director, Applied AI Innovation \& Business Adoption will drive the practical application, adoption, and measurable business impact of AI capabilities across NBCUniversal News Group.
This is a senior individual contributor role for a hands\-on AI practitioner who combines deep knowledge of modern AI tools with strong business acumen and stakeholder engagement skills, that requires the ability to influence without direct authority. Operating at the intersection of AI innovation, enablement, and business transformation, this individual will help News Group teams identify high\-value use cases, apply approved AI solutions to real workflows, and accelerate responsible adoption across the organization.
The ideal candidate is AI\-native, highly proactive, and comfortable translating emerging technologies into practical business outcomes. Success will be measured through adoption, productivity gains, workflow improvements, quality enhancements, cost avoidance, and other measurable business results.
Applied AI Innovation
- Drive AI adoption and innovation efforts across News Group functions, including editorial, product, technology, commercial, production, and operations.
- Partner with stakeholders to understand workflows, identify opportunities, and apply AI to improve efficiency, quality, creativity, and business outcomes.
- Lead AI engagement initiatives, including workshops, demos, office hours, use\-case discovery, pilots, and communications.
- Serve as a trusted advisor, helping News Group teams move from AI exploration to meaningful adoption.
Hands\-On AI Solutioning
- Apply approved AI tools directly to business use cases, developing workflows, prompts, prototypes, demonstrations, and practical recommendations.
- Translate business challenges into scalable AI\-enabled solutions.
- Identify reusable workflows and common use cases that can be expanded across the organization.
- Evaluate and recommend the best AI capabilities for specific business needs.
AI Strategy \& Market Awareness
- Monitor emerging AI technologies, product releases, and vendor roadmaps.
- Assess new capabilities and translate developments into actionable guidance for stakeholders.
- Maintain strong relationships with AI vendors and internal partners to identify opportunities for experimentation and adoption.
Stakeholder Engagement
- Build credibility with leaders and practitioners across the organization.
- Run and facilitate workshops, showcases, office hours, and adoption programs tailored to varying levels of AI maturity.
- Develop practical resources, playbooks, examples, and self\-service materials to support responsible AI use.
- Develop and support AI communities of practice and champion networks.
Measurement \& Business Impact
- Define and track adoption, engagement, and business impact metrics.
- Quantify outcomes including productivity gains, time savings, process improvements, quality enhancements, cost avoidance, and revenue opportunities.
- Communicate results, success stories, risks, and opportunities to leadership.
Cross\-Functional Collaboration
- Partner with Product, Technology, Data, Editorial, Operations, Learning \& Development, Legal, Security, and Governance teams within the News Group.
- Align AI adoption efforts with enterprise standards, product roadmaps, and responsible AI requirements.
- Act as a bridge between business users and technical teams within the News Group.
Required AI Expertise
Candidates should demonstrate deep hands\-on practical knowledge of:
- Generative AI applications for productivity, research, communication, content creation, planning, and analysis.
- Advanced prompting techniques and workflow design.
- AI\-enabled research, knowledge management, and collaboration workflows.
- AI\-assisted content, creative, editorial, data, and operational use cases.
- Automation and emerging agentic AI capabilities.
- Enterprise AI considerations including privacy, governance, security, responsible use, copyright, and risk management.
- Major enterprise AI platforms including Microsoft Copilot, OpenAI, Adobe, and similar solutions.
- Rapid evaluation and adoption of emerging AI technologies and capabilities.
Qualifications
- 10\+ years of experience in digital transformation, innovation, enterprise technology, product adoption, or related fields.
- 3 years of recent AI Adoption lead experience at the enterprise level.
- Deep hands\-on experience with modern AI platforms and enterprise AI tools.
- Proven ability to apply AI solutions directly to business workflows and deliver measurable outcomes.
- Experience leading adoption programs, workshops, demonstrations, and stakeholder engagement initiatives.
- Strong ability to identify opportunities, develop practical solutions, and drive adoption from concept through implementation.
- Demonstrated success defining metrics and measuring business impact.
- Excellent communication, stakeholder management, and influencing skills.
- Ability to thrive in a fast\-moving, evolving environment with multiple priorities.
- Experience presenting to executive and enterprise audiences.
- Ability to work on fast moving environment.
Desired Characteristics:
- Experience in media, journalism, digital publishing, technology, or other high\-velocity environments.
- Experience driving enterprise AI adoption or digital transformation initiatives.
- Experience developing communities of practice, champion networks, and enablement programs.
- Experience evaluating AI vendors and emerging technologies.
- Strong understanding of responsible AI, governance, privacy, security, and editorial considerations.
Additional Requirements:
Hybrid: This position currently has a hybrid schedule, which requires contributing from the office a minimum of four days per week. The Company reserves the right to change in\-office requirements at any time.
This position is eligible for company\-sponsored benefits, including medical, dental and vision insurance, 401(k), paid leave, tuition reimbursement, and a variety of other discounts and perks. Learn more about the benefits offered by NBCUniversal by visiting the Benefits page of the Careers website.
Salary range: $150,000 \- $200,000 (bonus and long\-term incentive eligible).
Additional Information
As part of our selection process, external candidates may be required to attend an in\-person interview with an NBCUniversal employee at one of our locations prior to a hiring decision. NBCUniversal's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law.
If you are a qualified individual with a disability or a disabled veteran and require support throughout the application and/or recruitment process as a result of your disability, you have the right to request a reasonable accommodation. You can submit your request to [email protected].
Salary Context
This $150K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At NBCUniversal, 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. This role's midpoint ($175K) sits 19% below the category median. Disclosed range: $150K to $200K.
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.
NBCUniversal AI Hiring
NBCUniversal has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Stamford, CT, US, New York, NY, US. Compensation range: $140K - $200K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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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