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About This Role
Make your mark at Comcast \- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award\-winning technology team that turns big ideas into cutting\-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on\-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.) Job Summary
Job Description Summary ABOUT DATABEE You will be an innovator within DataBee. (https://www.databee.ai/about) This division specializes in offering Software\-as\-a\-Service (SaaS) and subscription\-based security solutions to large enterprises and the federal government. For the first time ever, customers are now able to purchase some of the best of Comcast’s own in\-house security technologies. These solutions are proven\-at\-scale to defend critical infrastructure and effectively reduce cost. The Cybersecurity Suite efficiently improves security and compliance while keeping costs in check. Job Description Summary As a Senior Applied AI Engineer, you will be a core technical leader and individual contributor within DataBee’s exciting new cybersecurity business unit, which sells SaaS and subscription security solutions to large enterprises and the federal government. Working closely with engineering leadership, you will be primarily responsible for designing, developing, and deploying advanced analytics and data exploitation solutions for DataBee—a new SaaS security, risk, and compliance platform originally developed internally and used inside Comcast’s SOC, now made available to the Fortune 500 CISO customer base. You will operate in a fast\-paced, highly collaborative engineering environment to deliver exceptional value to DataBee customers. In this senior individual contributor role, you will extract knowledge and insights from high\-volume, high\-dimensional data to solve sophisticated business problems through advanced data preparation, modeling, analysis, and visualization techniques. You will utilize sophisticated statistical analysis, algorithms, predictive modeling, experimentation, and pattern recognition to consolidate and analyze unstructured, diverse big data sources, generating actionable insights and automated security solutions. While this is an individual contributor role, you will serve as a technical anchor and mentor within a globally distributed engineering team spanning the US and India, providing subject matter expertise, technical direction, and architectural guidance. You will help drive department efficiencies, champion industry best practices, and contribute to shaping a high\-energy startup culture within the DataBee business unit, empowering your peers to operate with agility and innovation.Job Description
Core Responsibilities
- Interacts with product and field engineering teams to identify questions and issues for data analysis and experiments.
- Leads development and coding of software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources.
- Uses analytical rigor and statistical methods to analyze large amounts of data, extracting practical insights using sophisticated statistical techniques such as data analysis, data mining, optimization tools and machine learning techniques and statistics
- Leads development and execution of statistical and mathematical solutions to business problems to support larger initiatives.
- Leads creation of data mining architectures/models/protocols, statistical reporting, and data analysis methodologies to identify trends in large data sets.
- Leads the production of analysis of historical patterns in customer behaviors and product performance from large, noisy, and complex datasets. Leads development and deployment of predictive models based on historical data that provide future predictions about customer behavior.
- Researches, educates and applies knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions.
- Crafts and is responsible for deliverables and presentations that report methodology and results of analysis.
- Owns the development of customer centric models and optimization tools to support large scale projects that use streaming and at\-rest processing over structured and unstructured data.
- Understand platform usage and assist with production deployments and customer issue triage.
- Develop security features as part of the development and adopt the DevSecOps culture.
- Use and improve on tools to identify and mitigate production incidents.
- Build re\-usable software components or libraries that can be used by multiple teams, where needed.
- Build self\-contained microservices or application programming interfaces to support the business logic.
- Use content management systems as applicable and global design patterns and defined coding standards and practices established by the team.
- Consistent exercise of independent judgment and discretion in matters of significance.
- Regular, consistent and punctual attendance. Must be flexible in schedule as vital.
- Other duties and responsibilities as assigned.
Additional Required Skills and Experience:
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- Expertise in Python.
- Experience developing and implementing microservices containing sophisticated analytical logic.
- Demonstrated expertise in working with cybersecurity data sets and applying machine learning techniques to aid in security compliance and/or threat hunting.
- Experience in parsing and cleansing data. Highly preferred if experienced in the application of machine learning to aid in data parsing and normalization.
- Experience designing, developing, and deploying applications powered by Large Language Models (LLMs), including conversational AI, chatbots, and AI assistants.
- Experience with Retrieval\-Augmented Generation (RAG), semantic search, vector databases, embeddings, and prompt engineering.
- Experience integrating foundation models (e.g., OpenAI, Anthropic, Gemini, or open\-source LLMs) into production applications through APIs and orchestration frameworks.
- Experience evaluating, fine\-tuning, and optimizing LLM\-based solutions, including model selection, prompt optimization, guardrails, and performance evaluation.
- Understanding of AI governance, responsible AI practices, model monitoring, and security considerations for generative AI applications.
- Experience building scalable AI/ML pipelines and deploying models using MLOps best practices.
- Experience working in Agile Scrum development environments.
- Experience working with GitHub and Jira or similar technologies.
- Experience using Python unit test frameworks.
- Experience using Docker and containerized application deployment.
- Experience developing on a SaaS product.
- Experience with distributed data processing technologies (e.g., Apache Spark, Apache Flink) is highly desirable.
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:
Data Science; Large Language Models (LLMs); Microservices Architecture; Software as a Service (SaaS); Python (Programming Language)
Salary:
National Pay Range: $98,678\.80 USD\-$231,278\.44 USD Illinois Pay Range: $104,846\.23 USD \- $203,525\.03 USD Colorado Pay Range: $111,013\.65 USD \- $212,776\.16 USD Hawaii Pay Range: $129,515\.93 USD \- $194,273\.89 USD Washington DC Pay Range: $141,850\.78 USD \- $212,776\.16 USD Maryland Pay Range: $117,181\.08 USD \- $212,776\.16 USD Minnesota Pay Range: $111,013\.65 USD \- $194,273\.89 USD New York Pay Range: $117,181\.08 USD \- $231,278\.44 USD Washington Pay Range: $111,013\.65 USD \- $222,027\.30 USD New Jersey Pay Range: $123,348\.50 USD \- $222,027\.30 USD Vermont Pay Range: $117,181\.08 USD \- $185,022\.75 USD Massachusetts Pay Range: $123,348\.50 USD \- $222,027\.30 USD Virginia Pay Range: $111,013\.65 USD \- $212,776\.16 USD Maine Pay Range: $111,013\.65 USD \- $185,022\.75 USD California Pay Range: $111,013\.65 USD \- $205,580\.83
Comcast intends to offer the selected candidate base pay within this range, 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.
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
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
7\-10 Years
Salary Context
This $111K-$231K range is below 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 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. This role's midpoint ($171K) sits 20% below the category median. Disclosed range: $111K to $231K.
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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