Comcast Cybersecurity: AI Development Research Engineer

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

The Comcast SPIDER team is seeking an Engineer II with a strong foundation in software development to help build AI-driven solutions and scalable data pipelines. You’ll collaborate with cybersecurity researchers, data engineers, software developers, and platform teams to turn business requirements into proof of concepts for cybersecurity tools and products. This role is perfect for early-career engineers who have completed internships or academic projects and are ready to contribute to real-world solutions.

Job Description

This position is ineligible for visa sponsorship.  To be considered for this role, you must be legally authorized to work in the United States and not require sponsorship for employment now or in the future.

Core Responsibilities

• Design & Build Data Pipelines: Develop batch and streaming pipelines for data ingestion, transformation, and quality validation using tools like Python, SQL, and/or cloud-native services.
• AI Application Development: Implement inference services, prompts/workflows, and retrieval pipelines (RAG) leveraging vector databases and embeddings.
• Data Engineering Fundamentals: Implement data models (dimensional/star schemas), optimize queries, and contribute to data lake/warehouse design.
• Performance Assessments: Perform functional testing for accuracy and implement iterative improvements.
• Documentation & Collaboration: Author technical specs, runbooks, and knowledge base articles; collaborate closely with cross-functional stakeholders.
• Security & Compliance: Follow secure coding practices and data governance policies. Implement security guardrails to enforce safe, read-only operations and prevent injection attacks. 
• Continuous Learning: Stay current on AI/ML and data engineering best practices; contribute to internal tooling, templates, and reusable components.
• Other duties and responsibilities as assigned.

Minimum Qualifications

• Bachelor’s degree in Computer Science, AI, Engineering, or related field; or equivalent practical experience.
• Proficiency in Python and SQL.
• Experience with data wrangling, ETL/ELT concepts, and working with relational and/or NoSQL databases.
• AI/ML Basics: Exposure to machine learning workflows (training, validation, inference) via coursework, projects, or internships; familiarity with libraries like NumPy, Pandas, SciPy, scikit-learn, PyTorch or TensorFlow.
• Familiarity with LLM frameworks (e.g., LangChain, LangGraph). 
• Internship/co-op or open-source contributions in data or AI.
• Strong problem-solving, communication, and teamwork skills.

Preferred Qualifications

• MS or PhD in CS, AI, Math, or related fields. 
• 2–3 years of experience building ML models and pipelines. 
• Experience with Databricks, or cloud data services (e.g., Azure Data Factory/Synapse, AWS Glue/Redshift).
• Experience with LLM workflows (prompt engineering, RAG, and vector databases).
• Familiarity with CI/CD (GitHub Actions) and Git.
• Familiarity with building agentic workflows. 
• Awareness of Cybersecurity principles. Knowing prompt engineering (LLM) security best practices (e.g., prompt injection) is a plus. 

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 custome