Machine Learning Engineer-Xumo

Comcast Resume • Irvine, CA

18d • $142,651 - $190,202 • Hybrid

About The Position

Xumo, a joint venture between Comcast and Charter Communications, develops streaming products and services through Xumo devices, Xumo Play, and Xumo Enterprise. The Machine Learning Engineer will work on the Xumo Software Engineering Team to solve business problems by designing, training, and deploying machine learning algorithms, especially the core recommendation system. The role develops predictive models using large datasets and creates scalable solutions that increase product engagement and personalize content for millions of viewers.

The engineer will collaborate with data, product, operations, backend, and client engineering teams to define requirements, engineer features and training data, evaluate models, and integrate recommendation algorithms into production systems. The role includes model experimentation and A/B testing, data pipeline management, model serving and latency improvements, technical documentation, proofs of concept, case studies, and contributions to patents and APIs. It also involves evaluating machine learning solutions, providing technical direction, mentoring junior engineers, and exercising independent judgment.

The position is office-based in Irvine, California, four days per week, with one day remote. Regular and punctual attendance is expected; nights and weekends may be required according to variable schedules. The posted estimated base-pay range is $142,651.46–$190,201.94 USD. A bachelor's degree is preferred, though Comcast may also consider coursework and experience or extensive related professional experience.

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.
• Knowledge and experience developing machine learning techniques, including clustering, decision-tree learning, and artificial neural networks, and understanding their real-world advantages and drawbacks.
• Knowledge and experience with 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, design, and implementation skills for solving new feature and problem-solving needs.
• Ability to work with minimal supervision and produce high-quality results.
• Ability and interest in learning new technologies and techniques.
• Bachelor's degree preferred; applicants may also be considered based on coursework and experience or extensive related professional experience.
• Relevant work experience listed as 5–7 years.

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.

Responsibilities

• Analyze large datasets to identify algorithmic insights, user behavioral features, and quantitative model-performance metrics.
• Engineer high-quality feature sets and training-data schemas for recommendation algorithms.
• Design and conduct offline model evaluations and online A/B tests, comparing performance with 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.
• Develop, tune, and scale recommendation models and multi-algorithm A/B testing logic.
• Collaborate with backend and client engineering teams to integrate models and improve model serving and latency.
• Create technical documentation covering algorithm behavior, dependencies, and system integration specifications.
• Make strategic decisions about model selection, hyperparameter optimization, and feature engineering.
• 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 production deployment.
• Create and maintain data pipelines for data 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 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 in matters of significance.
• Maintain regular, consistent, and punctual attendance, and work nights and weekends on variable schedules as necessary.
• Perform other duties and responsibilities as assigned.
• Follow Comcast's operating principles, prioritize customer experience, collaborate as a team, support inclusion, drive results and growth, and participate in the Net Promoter System.

Benefits

• Comcast provides benefits to eligible employees, with options and support intended to address physical, financial, and emotional needs.
• Most non-sales positions are eligible for a bonus.

What This Job Offers

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