Machine Learning Engineer-Xumo

Comcast • Irvine, CA

2d • Hybrid

About The Position

Xumo, a joint venture between Comcast and Charter Communications, develops a next-generation streaming platform through Xumo devices, Xumo Play, and Xumo Enterprise. The Machine Learning Engineer will work on the Xumo Software Engineering Team to design, train, and deploy sophisticated machine learning algorithms, including the core recommendation system. The role uses large datasets and tools such as Python, BigQuery, and modern machine learning frameworks to build personalization algorithms for streaming products and improve product engagement for millions of viewers.

The engineer will collaborate with product, operations, data, backend, and client engineering teams to translate business requirements into algorithmic solutions. Work includes creating features and training data, prototyping and productionizing recommendation models, evaluating models through offline analysis and online A/B tests, and integrating models into products with attention to serving and latency. The role also encompasses data pipeline management, technical documentation, evaluation of internal and external ML solutions, case studies, proofs of concept, patent and API contributions, and mentoring junior engineers.

This position is office-based in Irvine, California, with four days in the office and one day remote. The role may require nights and weekends and variable schedules as necessary. The stated pay range is $142,651.46–$190,201.94 USD. A bachelor’s degree is preferred, though applicants may also be considered based on coursework and experience or extensive related professional experience. Comcast describes additional benefits for eligible employees but directs applicants to its careers site for details.

Requirements

• At least 3 years of experience using statistical programming languages such as Python or R.
• At least 3 years of experience manipulating big data, such as with BigQuery.
• At least 3 years of experience operating databases such as MySQL, PostgreSQL, Oracle, or MongoDB.
• Experience and knowledge developing machine learning techniques such as clustering, decision trees, and artificial neural networks, including their real-world advantages and drawbacks.
• Experience and knowledge developing advanced statistical techniques and concepts, including regression, distribution properties, statistical tests, and their appropriate application.
• Strong problem-solving skills with an emphasis on product development.
• Excellent written, drawing, and verbal communication skills for cross-team coordination.
• Strong leadership and design and implementation skills for solving new-feature and other problems.
• Ability to work independently with minimal supervision while producing high-quality work.
• Willingness and ability to learn new technologies and techniques.
• Must be able to work nights and weekends and variable schedules as necessary.
• Regular, consistent, and punctual attendance.

Nice To Haves

• Experience operating servers in cloud environments such as Google Cloud, AWS, or Azure.
• Experience with data-processing frameworks such as Spark.
• Experience with Linux-based operating systems such as CentOS or OSX.
• Experience developing scalable and highly available applications.
• Experience with recommendation services.
• Bachelor’s degree; equivalent coursework and experience or extensive related professional experience may also be considered.

Responsibilities

• Analyze large datasets with the data team, extract user behavioral features, and define quantitative model-performance metrics.
• Engineer feature sets and training-data schemas for training and serving recommendation algorithms.
• Design and execute offline model evaluations and online A/B tests, comparing performance against baselines and benchmarks.
• Translate product and operations requirements into algorithmic solutions and document model architectures and logic.
• Prototype recommendation concepts, validate mathematical approaches, and transition experimental models into production-ready code.
• Implement, tune, and scale machine learning models and multi-algorithm A/B-testing logic for the recommendation system.
• Coordinate with backend and client engineering teams to integrate models, prioritize algorithmic improvements, and determine model-serving and latency-reduction approaches.
• Document algorithm behavior, model dependencies, and integration specifications for other systems.
• Make strategic decisions about model selection, hyperparameter optimization, and feature engineering to improve personalized content delivery.
• Develop and enhance machine learning algorithms for product and application integration, working with specifications and data-pipeline architectures.
• Lead model training and oversee validation and deployment into production systems.
• Create and maintain data pipelines, including ingestion, validation, cleaning, and monitoring.
• Author technical documentation and requirements, including evaluation plans, white papers, and reports.
• Contribute to patents, APIs, and other intellectual property.
• Evaluate machine learning solutions from internal and external partners and lead testing and assessment initiatives.
• Conduct case studies and compile findings to inform product development and testing strategies.
• Design and develop proof-of-concept solutions and direct studies supporting future products or applications.
• Collaborate with cross-functional teams and provide technical solutions to complex project issues.
• Guide and mentor junior engineers and set a technical example within the team.
• Exercise independent judgment and discretion; maintain regular, consistent, and punctual attendance; and perform other assigned duties.
• Follow company operating principles, prioritize customer experience, work collaboratively, support inclusion, and contribute to results and growth.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level