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About This Role
General Information
Locations: Austin, Texas, United States of America
- Location: Orlando
- State: Florida
- Country: United States of America
Role ID 216185 Worker Type Intern - Temporary Employee Studio/Department Fan Growth Work Model Hybrid
Description & Requirements
Electronic Arts creates next-level entertainment experiences that inspire players and fans around the world. Here, everyone is part of the story. Part of a community that connects across the globe. A place where creativity thrives, new perspectives are invited, and ideas matter. A team where everyone makes play happen. At Electronic Arts, we exist to Inspire the World to Play. Fan Care powers player support operations, experience management, and workflows that help our global teams to support world-class gaming experiences for millions of players worldwide.
The System Owners team operates at the intersection of enterprise tech, workflow strategy, and functional platform stewardship. We guide internal platforms and support ecosystems through every phase of their lifecycle. This includes leading research, intake, prioritization, requirements definition, and sprint planning to ensure our delivery platform remains modern, scalable, and efficient.
Position Overview
We are looking for an AI Enablement Intern to join the System Owners team within Fan Care for Summer 2027. You will scale and evolve our Agentic AI Framework — a system built to accelerate software delivery, operational workflows, and cross-platform integrations.
While partner engineering teams handle deep architecture, code execution, testing, and deployment, the System Owners team shapes the platform strategy — driving intake, research, functional requirements, and workflow prioritization. As an AI Enablement Intern, you will analyze how our teams work with agentic workflows, identifying functional gaps, and defining platform capabilities that enhance operational efficiency and system effectiveness. Promoting standardized, declarative instruction patterns (such as domain-specific skills.md architectures) will empower additional partners to adapt agentic workflows to their domains.
Responsibilities & Internship Project
- Agentic Framework Solution Enhancement: Define the functional requirements (BRDs, user stories, workflow maps) and operating models for a plug-and-play agentic framework. Help structure standardized skill layers (e.g., modular skills.md configurations) that allow additional teams to customize agent intake, business logic, and persona instructions without altering core code.
- Intake & Discovery: Conduct qualitative research and workflow analysis with our partners (Architecture, Engineering, QA, Fan Care Operations, and Support) to uncover pain points and high-value automation opportunities.
- Feature Backlog & Sprint Planning: Help translate business and operational needs into relevant backlog items for engineering teams building and refining AI agents.
- Enablement & Adoption: Develop documentation, playbooks, and best-practice guides to foster adoption and seamless integration of agentic workflows across Fan Care service delivery teams.
- Impact Measurement: Establish benchmarks to evaluate output accuracy, token efficiency, and productivity gains across different teams adopting the modular agentic framework.
Preferred Qualifications
- Currently enrolled in a Bachelor’s Degree granting program preferrable in Computer Science, Information Systems, Business Administration, Human-Computer Interaction, Artificial Intelligence, Product Management or a related field with an anticipated graduation date no earlier than December 2027.
- Conceptual knowledge about Agentic AI architecture (e.g., autonomous agents, tool usage, prompt orchestration, memory models, multi-agent frameworks).
- Familiarity with functional planning and delivery processes (Agile methodologies, user personas, business requirement documents, feature prioritization).
- Curiosity about enterprise software development lifecycles (SDLC), customer experience platforms, and modern IT operations.
- Excellent analytical, problem-solving, collaboration, and verbal/written communication skills, with the ability to work effectively across technical and non-technical teams.
Nice-to-Have Skills
- Hands-on experience with LLM frameworks, APIs, or agent builders (e.g., LangChain, AutoGen, CrewAI, OpenAI APIs, Google Gemini Enterprise).
- Experience with markdown-driven agent prompt architectures, contextual memory management, or RAG systems.
- Familiarity with enterprise workflow tools (Jira, Confluence) and API/integration concepts.
What You'll Learn & Experience
- Direct Impact: Work on a tangible project that directly shapes the next iteration of Fan Care's internal AI tooling.
- Mentorship & Leadership Exposure: Collaborate with experienced System Owners, functional leads, and Fan Care leaders.
- End-to-End Solution Lifecycle: Gain practical experience taking AI features from initial problem discovery and requirements definition through to sprint planning and rollout.
- Gaming Industry Insights: Connect with interns and leaders across Electronic Arts through structured networking, skill-building workshops, and executive speaker series.
About Electronic Arts We’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.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Electronic Arts, 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 $212,000 based on 11,857 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Electronic Arts AI Hiring
Electronic Arts has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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