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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
We build and design the tools of tomorrow to stop bad actors from harming our customers and our company. Our mission is to build, maintain, and advance the next generation capabilities that power Comcast’s global cybersecurity operations. This team owns the Premonition platform, which manages, deploys, and composes agentic pipelines at scale—combining large language models, high\-performance data systems, and modern cloud infrastructure to give our security teams a decisive edge. We work with technologies like Python, SQL, Rust, JavaScript, LLMs (via Ollama, Bedrock, SageMaker), AWS, EKS, open table formats (Iceberg, Delta, Hudi), and embedded engines such as DuckDB and LanceDB. Our culture is supportive, caring, competitive, high ownership, and deeply technical: we help each other grow, we care about people as much as outcomes, and we push ourselves to solve hard problems at scale. If you want to apply advanced LLMs and high\-performance data engineering to real\-world cybersecurity threats affecting millions of customers, this is the place to do it.Job Description
As Cyber Security Engineer on the Cybersecurity AI \& Data Platforms team, you will develop and maintain Premonition, Comcast’s internal platform for managing, deploying, and composing agentic pipelines that power global cybersecurity operations.
You will split your time between:
- building a robust, scalable platform for LLM and agent driven workflows, and
- optimizing the high\-performance data layer supporting those workflows, including storage formats, compression, latency, and query performance across large datasets.
This role sits at the intersection of applied AI and big data engineering. You will work with large language models, orchestration frameworks, and cyber focused agentic pipelines, while also owning the data and infrastructure foundations that make those systems fast, reliable, and cost\-effective at Comcast scale. You’ll partner closely with security operations, incident response, and threat hunting teams to translate real\-world workflows into secure, composable, and observable agentic systems.
Key Responsibilities
- Design, build, and maintain the Premonition platform for managing, deploying, and monitoring agentic pipelines.
- Develop composable building blocks—agents, tools, pipelines, evaluators, and configuration—to enable rapid workflow assembly and iteration.
- Implement robust APIs, orchestration, and infrastructure integrations on AWS and EKS for reliable LLM and agent driven workloads.
- Engineer and optimize the high performance data layer, including Iceberg/Delta/Hudi, compression, indexing, latency, and largescale query performance.
- Work with embedded engines such as DuckDB and LanceDB to enable interactive, low latency analytics.
- Collaborate with cybersecurity stakeholders (SOC, IR, threat hunters, engineers) to translate workflows into secure, automated, observable pipelines.
- Experiment with and productionize LLM and agent patterns (RAG, tool use, multistep agentic workflows), including evaluation, safety, and guardrails.
- Own the full lifecycle of Premonition services: design, implementation, testing, deployment, observability, performance tuning, and continuous improvement.
- Contribute to and enforce standards and best practices for LLM/agent usage, data management, security, and governance.
- Participate in design/code reviews and foster a supportive, caring, competitive, high ownership, deeply technical culture.
Required Qualifications
- Strong production experience with Python.
- Solid SQL skills and experience working with large datasets.
- Experience designing, building, and operating production APIs or microservices, including testing, observability, CI/CD.
- Experience running workloads on AWS and Kubernetes/EKS.
- Experience with high performance data engineering, including:
+ Iceberg, Delta, or Hudi; or
+ optimizing storage, compression, latency, and analytical query performance.
- Ability to work in a deeply technical, high ownership environment and collaborate effectively across functions.
- Strong communication skills with the ability to work closely with security stakeholders and translate workflows into technical designs.
Preferred Qualifications
- Demonstrated experience building LLM based applications or agentic workflows (not just prompt tinkering).
- Hands on experience with agentic workflows: agents, tools, orchestration, multistep pipelines.
- Experience with LLM platforms: Ollama, Amazon Bedrock, SageMaker.
- Familiarity with orchestration frameworks: LangChain, LlamaIndex, or equivalent internal tooling.
- Production experience with Iceberg, Delta Lake, or Hudi.
- Experience with DuckDB or LanceDB.
- Experience with Rust and/or JavaScript/TypeScript.
- Experience with workflow/orchestration tools: Airflow, Temporal, Argo.
- Prior exposure to cybersecurity domains (SOC, IR, threat hunting, security engineering).
- Experience in largescale enterprise or telecom environments with high security/reliability/compliance requirements.
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:
Amazon Web Services (AWS); Structured Query Language (SQL); Python (Programming Language); Big Data Engineering
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 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: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($171K) sits 22% below the category median. Disclosed range: $111K to $231K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Comcast AI Hiring
Comcast has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Mount Laurel, NJ, US, Philadelphia, PA, US, New York, NY, US. Compensation range: $157K - $242K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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