Interested in this AI/ML Engineer role at Comcast?
Apply Now →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
The Senior Specialist, Agentic Sales Assist supports the execution and continuous improvement of Comcast’s AI-powered assisted sales enablement program. Working within the Agentic Sales Assist team, this role helps translate enterprise strategy into disciplined execution across sales channels by coordinating priorities, platform activities, stakeholder inputs, analytics, and operational follow-through. This role operates at the intersection of AI-enabled sales technology, sales performance, program operations, and frontline enablement. The Senior Specialist will help maintain the operating rhythms, governance processes, project plans, and performance insights needed to scale HeroAssist and related agent-assist capabilities while ensuring work remains grounded in real agent behaviors and measurable business outcomes. Why This Role Matters • Supports a highly visible sales transformation that reaches assisted sales teams across Comcast. • Helps turn AI-enabled sales strategy into repeatable processes, deliverables, and measurable improvements. • Connects frontline feedback, platform performance, analytics, and stakeholder priorities to improve the agent experience. • Provides hands-on program support in an environment where standards, governance, and operating rhythms continue to mature. Training & Onboarding Experience Because the platform and operating model continue to evolve, onboarding will emphasize hands-on learning and cross-functional immersion: • Orientation to the Agentic Sales Assist strategy, team structure, governance model, and operating principles. • Platform training and hands-on learning with HeroAssist / Cresta capabilities and workflows. • Stakeholder introductions across sales, product, analytics, enablement, operations, and vendor partners. • Project shadowing, working sessions, and frontline observation to build context on agent and leader needs.Job Description
Core Responsibilities
Program Execution & Governance
- Support execution of the Agentic Sales Assist roadmap, priorities, and phased delivery plans
- Maintain governance artifacts, decision logs, intake processes, action trackers, project plans, and recurring operating cadences.
- Coordinate cross-functional working sessions and ensure decisions, owners, dependencies, and next steps are documented and followed through.
- Help evaluate incoming requests based on business impact, readiness, urgency, dependencies, and cross-channel applicability.
- Exercise independent judgment in organizing work, surfacing risks, and escalating issues that require leadership decisions.
- 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) and overtime as necessary.
- Other duties and responsibilities as assigned.
Platform & Capability Operations
- Develop working proficiency in the AI-powered sales enablement platform and support day-to-day program operations.
- Coordinate with build/configuration teams and vendor partners on behavior, workflow, reporting, access, defect, and enhancement needs.
- Support validation of new or optimized capabilities by tracking requirements, test evidence, launch readiness, and post-launch follow-up.
- Help maintain platform access, documentation, standards, and operational processes in partnership with appropriate administrators and stakeholders.
Performance, Analytics & Continuous Improvement
- Compile and organize adoption, behavior, platform, and sales performance insights for recurring business reviews.
- Partner with analytics and business teams to translate data and agent feedback into clear findings, recommendations, and follow-up actions.
- Track whether capabilities are firing as intended, being used as intended, and contributing to target business outcomes.
- Identify recurring issues, adoption barriers, quality gaps, and optimization opportunities and route them through the appropriate governance process.
- Support executive and operational reporting with concise summaries of progress, risks, decisions, and measurable impact.
Cross-Channel & Stakeholder Coordination
- Partner with sales channel leaders, enablement, product, analytics, operations, and other cross-functional teams to coordinate implementation and adoption activities.
- Facilitate stakeholder communications, working sessions, and feedback loops that connect frontline experience to program improvements.
- Coordinate with external vendor partners on deliverables, issue resolution, training, platform capabilities, and follow-up actions.
- Manage multiple concurrent workstreams and shifting deadlines while maintaining strong attention to detail and accountability.
Minimum Qualifications
- 5–7 years of experience in program management, project management, sales operations, enablement, technology operations, analytics, or a related field.
- Strong project-management and organizational skills with the ability to manage multiple workstreams, owners, dependencies, and deadlines.
- Ability to work effectively in a multidimensional, cross-functional environment and influence progress without direct authority.
- Strong analytical and problem-solving skills with the ability to synthesize data, feedback, and operational information into an actionable point of view.
- Excellent written and verbal communication skills, including the ability to create clear executive-ready updates and facilitate working sessions.
- High attention to detail, strong follow-through, and a demonstrated sense of accountability.
- Comfort learning and administering technology platforms, workflows, data, permissions, and business processes.
- Proficiency with Microsoft Office applications, including PowerPoint, Excel, Word, Teams, OneDrive, and SharePoint.
Preferred Skills & Capabilities
- Experience supporting sales, contact center, sales enablement, or frontline operations.
- Understanding of how sales behaviors, coaching, adoption, and agent workflows influence business outcomes.
- Experience with AI-enabled tools, sales technology, conversational intelligence, workflow platforms, or similar capabilities.
- Experience working with analytics, dashboards, performance measurement, or business-insight teams.
- Experience coordinating vendor relationships or technology partners.
- Comfort operating in an evolving environment where processes and playbooks are still being established.
90-Day Success Profile
- A successful Senior Specialist in the first 90 days will be able to:
- Clearly explain the Agentic Sales Assist strategy, operating model, roadmap, and key stakeholder roles.
- Independently use the core enablement platform and navigate the primary program workflows and governance processes.
- Maintain reliable project plans, action tracking, recurring business reviews, and decision documentation.
- Build productive relationships with sales, analytics, enablement, product, operations, and vendor partners.
- Synthesize platform, adoption, performance, and frontline inputs into concise insights and recommended actions.
- Own assigned workstreams from intake through execution, follow-up, and measurable closure.
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:
Sales Operations; Customer Experience (CX); Problem Solving; Accountability; Sales Management
Salary:
Pay Range: This job can be performed in Denver Campus with a Pay Range of $66,280.84 - $109,363.39
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
5-7 Years
Salary Context
This $66K-$109K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($87K) sits 59% below the category median. Disclosed range: $66K to $109K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Comcast AI Hiring
Comcast has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Irvine, CA, US, Philadelphia, PA, US, New York, NY. Compensation range: $109K - $190K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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