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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 AI Solutions Program Associate Lead will support the strategic alignment between the Prism team and NBCUniversal Business Units. The successful candidate will collaborate with the BU Stakeholders, Product and Engineering teams to support the successful execution of AI programs as well as process improvement activities in support of their respective BU. They will contribute to the delivery of AI solutions and operational support inclusive of Intake process, discovery, scope, and defining project phases across their assigned BU and Prism. This role will also assist in service delivery, program delivery, and AI operational support, representing services across Prism.
Responsibilities:
The AI Solutions Program Associate Lead will support the maintenance of a clear, centralized process for AI initiatives intake, prioritization and value stories for their individual BU, Technical Project or Program. They will help maintain a short\\long term roadmap that aligns with the overall BU, Prism Technologies and Key Initiatives, and will ensure process alignment for BAU tasks and issue resolution and will support the end\-to\-end delivery of strategic AI initiatives in support of the BU. They will assist in the execution of specific BU AI Programs as well as other projects as assigned.
- Support the delivery of AI solutions and operational support for the BUs, Prism Technologies or Key Projects.
- Assist in the delivery of AI solutions and operational support inclusive of discovery, scope, defining project phases and on\-going support inclusive of hypercare \& BAU transitions
- Help represent the capabilities of Prism and the evolution of the business strategy.
- Contribute to the development and alignment of near\-term and long\-term roadmaps for each BU by supporting requirements scoping, LRP assessment, and request prioritization.
- Provide clear communication on requirements, risks, impacts and strategy to working teams inclusive of engineering, product, and cyber teams. Support communication to senior leadership as needed and assist with risk and issue mitigation.
- Build relationships across business partners and team leads and be available for engagement needs as directed.
- Support Prism teams by sharing relevant business context and escalating needs appropriately
- Assist in coordinating project status meetings to monitor project health and budget, and help escalate major issues to senior team members.
- Develop subject matter expertise for his/her assigned BU or program and provide support on existing platforms and business processes to project teams.
- Support business leads in securing project acceptance, hand\-off from implementation to operational teams, and adoption of new platforms.
- Help standardize tools, templates, and methodologies for use in major efforts and support consistent application across projects.
- Provide hands\-on support in analyzing and resolving operational challenges
- Support change management efforts by identifying AI opportunities, proposing solutions, and contributing to process/technical innovation
- Assists in analyzing, evaluating, and mitigating program risks, and contributes to program reports for management. Works under the direct supervision of the AI Solutions and Programs Lead and the Director of AI Solutions and Programs
- Responsible for completing any other tasks as assigned by manager as needed.
Qualifications
- 2\+ years of work experience working with large program / project management, Business engagement Solutions or Technical Operations.
- Developing communication and interpersonal skills, including relationship building and collaboration within cross\-functional teams.
- Strong oral and written communication skills, with excellent presentation and facilitation skills
- Ability to prioritize effectively, think independently and problem solve to ensure all projects are completed in an effective and timely manner
- Results\-oriented with a history of problem resolution
- Ability to manage a high volume of details with excellent accuracy
- Effectively handle sensitive and confidential matters
- Always provide positive solutions to the problem. Strong sense of ownership
- Appropriate sense of urgency and commitment for critical deliverables
- Adaptable with ability to work effectively under pressure and through significant change
- A true collaborator and team player with a positive attitude who can encourage and promote one team across groups to meet common goals and add value.
- Attention to details and ability to work independently and in a fast environment
- Bachelor's degree in Computer Science, Information Systems, Business Management, or related field required.
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: $90,000 \- $115,000
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 $90K-$115K range is in the lower quartile 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 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 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($102K) sits 43% below the category median. Disclosed range: $90K to $115K.
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.
NBCUniversal AI Hiring
NBCUniversal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $115K - $115K.
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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