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Data & AI Engineering Intern (Master's Level) - Summer 2027

Electronic Arts
Austin, USA
Intern
Hybrid
Discovered 2 weeks ago
awsazuregcpgitpythonsnowflake

Full Job Posting

Responsibilities

  • Develop AI Workflows: Build and evaluate LLM or agent workflows using prompts, RAG (Retrieval-Augmented Generation), APIs, tool calling, and structured outputs.
  • Build Engineering Components: Develop reliable Python and SQL components, tools, and pipelines for data ingestion, transformation, validation, testing, and data-quality checks.
  • Ensure Quality & Safety: Review AI outputs with mentors, implement safety safeguards/guardrails, document design decisions, and systematically evaluate agent performance.
  • Own a Deliverable: Define expected outcomes for a scoped agentic data-engineering use case, deliver a functional prototype, and explain its business value to technical and business stakeholders.
  • Collaborate & Learn: Participate in team meetings, pair programming, architecture reviews, and network with the broader EA engineering and data organization.
  • Grow Technical Skills: Gain exposure to responsible AI, data security, privacy, cloud data engineering, and agile software development practices.

Qualifications

  • Currently pursuing a graduate degree in Computer Science, Data Engineering, Data Science, or a related technical field.
  • Generative AI Exposure: Hands-on exposure to LLMs through coursework or projects, with the ability to clearly explain the use case, your individual contribution, approach, and results.
  • Agentic AI Foundations: Basic knowledge of RAG, embeddings, tool/function calling, structured outputs, workflow orchestration, guardrails, or human-in-the-loop systems.
  • Data Engineering Foundations: Familiarity with Python, SQL, relational databases, APIs, data transformation, and basic pipeline or ETL/ELT concepts.
  • Agile Mindset: Evidence of self-directed learning, smart problem-solving skills, curiosity, and clear communication.
  • Nice-to-Haves: Prior exposure to cloud data environments (e.g., Snowflake, AWS, Azure, GCP), Git, or Machine Learning (ML) is a bonus but not required.
  • You must be available for a full-time paid internship in the Summer of 2027
  • At this time, we are considering Undergrad students who will be enrolled in an accredited degree program in the summer of 2027, slated to graduate no earlier than December 2027. Applicants must be legally authorized to work in the United States on a full-time basis during the 12 week internship. Visa sponsorship is not available for this position.
  • Please ensure that your graduation date is visible on your resume.
  • About Electronic ArtsWe’re proud to have an extensive portfolio of games and experiences, locations around the world, and opportunities across EA. We value adaptability, resilience, creativity, and curiosity. From leadership that brings out your potential, to creating space for learning and experimenting, we empower you to do great work and pursue opportunities for growth.We adopt a holistic approach to our benefits programs, emphasizing physical, emotional, financial, career, and community wellness to support a balanced life. Our packages are tailored to meet local needs and may include healthcare coverage, mental well-being support, retirement savings, paid time off, family leaves, complimentary games, and more. We nurture environments where our teams can always bring their best to what they do.Electronic Arts is an equal opportunity employer. All employment decisions are made without regard to race, color, national origin, ancestry, sex, gender, gender identity or expression, sexual orientation, age, genetic information, religion, disability, medical condition, pregnancy, marital status, family status, veteran status, or any other characteristic protected by law. We will also consider employment qualified applicants with criminal records in accordance with applicable law. EA also makes workplace accommodations for qualified individuals with disabilities as required by applicable law.

Preferred Qualifications

  • Nice-to-Haves: Prior exposure to cloud data environments (e.g., Snowflake, AWS, Azure, GCP), Git, or Machine Learning (ML) is a bonus but not required.

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