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About This Role
Comcast brings together the best in media and technology. We drive innovation to create the world's best entertainment and online experiences. As a Fortune 50 leader, we set the pace in a variety of innovative and fascinating businesses and create career opportunities across a wide range of locations and disciplines. We are at the forefront of change and move at an amazing pace, thanks to our remarkable people, who bring cutting\-edge products and services to life for millions of customers every day. If you share in our passion for teamwork, our vision to revolutionize industries and our goal to lead the future in media and technology, we want you to fast\-forward your career at Comcast. Job Summary
Responsible for working cross\-functionally to collect data and develop models to determine trends utilizing a variety of data sources. Retrieves, analyzes and summarizes business, operations, employee, customer and/or economic data in order to develop business intelligence, optimize effectiveness, predict business outcomes and decision\-making purposes. Involved with numerous key business decisions by conducting the analyses that inform our business strategy. This may include: impact measurement of new products or features via normalization techniques, optimization of business processes through robust A/B testing, clustering or segmentation of customers to identify opportunities of differentiated treatment, deep dive analyses to understand drivers of key business trends, identification of customer sentiment drivers through natural language processing (NLP) of verbatim responses to Net Promotor System (NPS) surveys and development of frameworks to drive upsell strategy for existing customers by balancing business priorities with customer activity. Has in\-depth experience, knowledge and skills in own discipline. Usually determines own work priorities. Acts as resource for colleagues with less experience.Job Description
Core Responsibilities
- Supporting the end\-to\-end delivery of digital experiments across the Digital Sales and Service ecosystem of the residential Comcast Cable business including our Xfinity website, IVR (integrated Voice Response), Xfinity Assistant (virtual assistant), mobile apps, and platforms.
- Proactively identify, prioritize, and conduct analyses that drive meaningful change on key objectives established by senior Comcast leadership
- Synthesize actionable insights by joining together large data sets from across the company, and applying appropriate analytical techniques
- Communicate findings clearly and concisely through narrative\-driven presentations and effective data visualizations to Comcast executives and decision makers
- Works with business leaders and stakeholders to understand data and analysis needs and develop technical requirements.
- Determines relevancy of data and identifies alternative data sources as needed to ensure analytics requested by business stakeholders can be completed.
- Analyzes large, complex data to determine actionable business insights using self\-service analytics and reporting tools.
- Queries disparate big data platforms such as Teradata, SQL Server, Hadoop, and AWS.
- Combines data as needed from disparate data sources to complete analysis.
- Identifies key business drivers and insights by conducting exploratory data analysis and hypothesis testing.
- Promotes and evangelizes a culture of excellence, data driven discussions, healthy skepticism, intellectual curiosity, knowledge sharing, and teamwork.
- Stays current with emerging trends in analytics, statistics, and machine learning and applies them to business challenges.
- Consistent exercise of independent judgment and discretion in matters of significance.
- Regular, consistent and punctual attendance. Must be able to work nights and weekends, variable schedule(s) as necessary.
- Other duties and responsibilities as assigned.
Employees at all levels are expected to:
Understand our Operating Principles; make them the guidelines for how you do your job.
Own the customer experience \- think and act in ways that put our customers first, give them seamless digital options at every touchpoint, and make them promoters of our products and services.
Know your stuff \- be enthusiastic learners, users and advocates of our game\-changing technology, products and services, especially our digital tools and experiences.
Win as a team \- make big things happen by working together and being open to new ideas.
Be an active part of the Net Promoter System \- a way of working that brings more employee and customer feedback into the company \- by joining huddles, making call backs and helping us elevate opportunities to do better for our customers.
Drive results and growth.
Support a culture of inclusion in how you work and lead.
Do what's right for each other, our customers, investors and our communities.
Disclaimer:This information has been designed to indicate the general nature and level of work performed by employees in this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications.
Comcast is an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law.
Skills:
Experimentation; Exploratory Data Analysis (EDA); Big Data; Curious Mindset; Analytics
Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non\-sales positions are eligible for a Bonus. Additionally, Comcast provides best\-in\-class Benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That’s why we provide an array of options, expert guidance and always\-on tools, that are personalized to meet the needs of your reality \- to help support you physically, financially and emotionally through the big milestones and in your everyday life. Please visit the compensation and benefits summary on our careers site for more details.
Education
Bachelor's Degree
While possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience.
Relevant Work Experience
5\-7 Years
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 Comcast, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Comcast AI Hiring
Comcast has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span Philadelphia, PA, US, New York, NY, US, Reston, VA, US. Compensation range: $152K - $351K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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