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About This Role
Xumo, a joint venture between Comcast and Charter Communications, was formed to develop and offer a next-generation streaming platform for the entire entertainment industry. The company consists of three primary lines of business: Xumo devices, Xumo Play, and Xumo Enterprise. Powered by Comcast’s global entertainment platform, Xumo devices feature a world-class user interface with universal voice search capabilities, making it easy for consumers to find and enjoy their favorite streaming content. Xumo Play is a free ad-supported streaming TV (FAST) service offering hundreds of linear channels and on-demand options. Xumo Enterprise provides tools and services for content creators, distributors, and advertisers to make FAST content more accessible. Job Summary
XUMO is looking for a highly experienced and motivated ML Engineer to solve business problems by designing and training sophisticated machine learning algorithms and implementing our core recommendation system, which will collaborate with multiple systems to increase product engagement. As the ML Engineer for the XUMO Software Engineering Team, you’ll develop complex predictive models using huge amounts of data, create scalable algorithmic solutions to solve business problems, and work with team members to architect and lead the development of the recommendation engine's intelligence. Utilizing tools like Python, BigQuery, and modern ML frameworks, you will build the personalization algorithms that immerse millions of viewers through streaming devices. To succeed in this role, it is necessary to continuously provide clear technical solutions and algorithmic strategies which satisfy the business requirements from the product and operation teams. This job focuses on developing machine learning algorithms for products, training models, and deployment. It involves data pipeline management, technical documentation, and innovation via patents and APIs. The role evaluates ML solutions, conducts case studies, and designs proofs of concept. Team collaboration and junior engineer mentorship are key components.Job Description
Position Duties:
Analyze big data to drive algorithmic insights
- Collaborate with the data team to analyze massive datasets, extracting key user behavioral features and defining quantitative metrics to evaluate model performance.
- Engineer high-quality feature sets and training data schemas optimized specifically for training and serving recommendation algorithms.
- Design and execute rigorous offline model evaluations and online A/B tests, generating deep insights on algorithmic performance against established baselines and benchmarks.
Development of Recommendation System
- Collaborate with product and operations teams to translate business requirements into algorithmic solutions, documenting model architectures and logic to align development tasks.
- Rapidly prototype new recommendation concepts and algorithms, validating mathematical approaches and transitioning experimental models into high-quality, production-ready code.
- Develop the core intelligence of the recommendation system, focusing on implementing, tuning, and scaling advanced machine learning models and the logic for multi-algorithm A/B testing.
Plan and manage recommendation algorithm development
- Collaborate with backend and client engineering teams to integrate machine learning models seamlessly, prioritizing algorithmic enhancements and defining the optimal approaches for model serving and latency reduction.
- Create comprehensive technical documentation detailing algorithmic behavior, model dependencies, and the specifications required for other systems to interact with the recommendation engine.
- Drive strategic decisions regarding model selection, hyperparameter optimization, and feature engineering to solve complex business problems, continuously improving the user experience through highly personalized content delivery.
Qualifications
- 3+ years’ experience of using statistical computer languages (Python, R , etc.)
- 3+ years’ experience of manipulating big data (Big Query, etc.)
- 3+ years’ experience of operating database (MySQL, PostgreSQL, Oracle, MongoDB)
- Experienced and has knowledge of developing variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Experienced and has knowledge of developing advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
- Strong problem-solving skill with an emphasis on product development
- Excellent written, drawing, and verbal communication skills for coordinating across teams.
- Strong leads with designing, implementation skills to approach the solution for any new features or problem solving
- Self-starter to able to work with minimal supervision for high-quality output
- Aggressive learner for new technologies and techniques.
- Position is office based in Irvine, CA 4 days/week in office & 1 day remote.
Desirable Experiences
- Experience with operating servers on cloud-based environments (Google Cloud, AWS, Azure)
- Experience with data processing framework (Spark)
- Experience with working with Linux-based operation system (CentOS, OSX)
- Experience developing scalable and highly available applications
- Experience with recommendation services
Responsibilities:
- Developing and enhancing machine learning algorithms for product and application integration, working with specifications and data pipeline architectures
- Leading the training of machine learning models, overseeing their validation and deployment into production systems
- Creating and maintaining data pipelines, ensuring efficient data ingestion, validation, cleaning, and monitoring processes
- Authoring comprehensive documentation and technical requirements, including evaluation plans, white papers, and reports
- Contributing to the creation of patents, Application Programming Interfaces (APIs), and other intellectual property
- Evaluating machine learning solutions from internal and external partners, leading testing and assessment initiatives
- Conducting in-depth case studies and compile findings to inform product development and testing strategies
- Designing and developing proof of concept solutions, directing studies that support the advancement of future products or applications
- Collaborating with cross-functional teams and provide technical solutions to complex issues associated with assigned projects
- Offering guidance and mentorship to junior engineers, setting a technical example within the team
- 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:
Collaboration; Algorithms; Recommendation Engines; Business Problems
Salary:
Pay Range: This job can be performed in California with a good faith estimated pay range upon hire of $142,651.46 - $190,201.94 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
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 $142K-$190K range is below the median 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 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($166K) sits 21% below the category median. Disclosed range: $142K to $190K.
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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