Interested in this AI/ML Engineer role at Comcast?
Apply Now →About This Role
FreeWheel, a Comcast company, provides comprehensive ad platforms for publishers, advertisers, and media buyers. Powered by premium video content, robust data, and advanced technology, we’re making it easier for buyers and sellers to transact across all screens, data types, and sales channels. As a global company, we have offices in nine countries and can insert advertisements around the world. Job Summary
We are seeking a Sr Principal Scientist of Research \& Optimization Sciences to lead a world\-class organization of research scientists and machine learning experts focused on developing the next generation of optimization, prediction, targeting, and marketplace systems. This executive will define the scientific vision, research agenda, and technical strategy for large\-scale advertising and marketing platforms, driving innovations that directly impact revenue, customer performance, and marketplace efficiency. The ideal candidate combines deep expertise in machine learning, optimization, economics, operations research, and experimentation with a proven ability to lead globally distributed research organizations. This leader will translate cutting\-edge research into production systems operating at internet scale while partnering closely with Product, Engineering, Data Science, and Business leadership.Job Description
Key Responsibilities:
### Research Leadership
- Define and execute the long\-term research strategy for optimization, targeting, bidding, forecasting, and marketplace systems.
- Lead a global team of research scientists and applied researchers across multiple geographies.
- Identify emerging opportunities in machine learning, artificial intelligence, optimization, and computational economics.
- Foster a culture of scientific rigor, innovation, experimentation, and measurable business impact.
### Machine Learning \& Optimization
- Drive development of large\-scale machine learning systems for prediction, recommendation, targeting, and campaign optimization.
- Advance state\-of\-the\-art approaches in:
+ Predictive modeling
+ Reinforcement learning
+ Causal inference
+ Auction theory
+ Multi\-objective optimization
+ Control systems
+ Operations research
- Oversee research from problem formulation through experimentation, deployment, and production monitoring.
### Marketplace \& Platform Strategy
- Lead innovation across demand\-side platform (DSP) optimization, campaign delivery, supply optimization, and audience targeting systems.
- Partner with Product and Engineering leaders to align research investments with strategic business objectives.
- Develop frameworks that improve advertiser outcomes, platform efficiency, and marketplace health.
### Organizational Leadership
- Recruit, mentor, and retain top\-tier scientific talent.
- Establish technical standards, research processes, and career development frameworks.
- Build collaborations across research, engineering, product, and commercial organizations.
- Represent the company externally through publications, patents, industry conferences, and academic partnerships.
### Business Impact
- Translate advanced research into measurable improvements in revenue, efficiency, customer performance, and platform scalability.
- Drive data\-driven decision\-making through experimentation and causal measurement frameworks.
- Evaluate strategic technology investments and emerging opportunities.
Required Qualifications
---------------------------
- Ph.D. in Electrical Engineering, Computer Science, Operations Research, Applied Mathematics, Statistics, Economics, or related quantitative field.
- 15\+ years of experience developing machine learning and optimization systems.
- 10\+ years of leadership experience managing research scientists and technical organizations.
- Demonstrated success deploying research innovations into production environments at scale.
- Deep expertise in several of the following:
+ Machine Learning
+ Optimization
+ Operations Research
+ Control Theory
+ Economics and Auction Design
+ Statistical Modeling
+ Large\-Scale Experimentation
- Track record of publications, patents, or significant scientific contributions.
- Exceptional communication and executive stakeholder management skills.
- Experience leading research organizations within digital advertising, marketing technology, marketplaces, or internet\-scale platforms.
- Expertise in demand\-side platforms (DSPs), bidding systems, targeting technologies, or advertising optimization.
- Experience managing globally distributed teams.
- Strong record of academic and industry thought leadership.
- ### 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.
Success Metrics
-------------------
- Measurable improvements in platform performance and optimization outcomes.
- Successful deployment of research innovations into production systems.
- Growth and retention of scientific talent.
- Patent generation and publication output.
- Revenue impact attributable to optimization and machine learning initiatives.
- Strategic advancement of the company's AI and optimization capabilities.
### Ideal Candidate Profile
A recognized leader in applied machine learning and optimization research who can bridge cutting\-edge science and business outcomes. This executive is equally comfortable discussing auction theory, reinforcement learning, organizational strategy, and executive\-level business priorities, while building teams capable of solving some of the industry's most challenging large\-scale optimization problems.
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. Comcast will consider for employment applicants with arrest or conviction records in accordance with the requirements of applicable law, including the San Francisco Fair Chance Ordinance, the Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Please note that federal state, or local laws and regulations may restrict or prohibit Comcast from hiring individuals convicted of certain crimes. Additionally, an applicant’s criminal history may have a direct, adverse, and negative relationship on the job duties of this position, which may result in the withdrawal of a conditional offer of employment.
Skills:
Leadership; Mathematics Modeling; Artificial Intelligence (AI); Innovation; Business; Team\-Building
Salary:
Primary Location Pay Range: This job can be performed in California with a good faith estimated pay range upon hire of $288,594\.24 \- $384,792\.31 USD.
Comcast intends to offer the selected candidate base pay within the posted range for this role at the time of posting dependent on job\-related, non\-discriminatory factors such as experience. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.
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
Ph. D.
Relevant Work Experience
15 Years \+
Salary Context
This $288K-$384K range is above the 75th percentile 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 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 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 ($336K) sits 86% above the category median. Disclosed range: $288K to $384K.
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.
Comcast AI Hiring
Comcast has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Philadelphia, PA, US, San Francisco, CA, US, Washington, DC, US. Compensation range: $209K - $384K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% 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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