Electronic Arts

EA Experiences group (XO) is dedicated to ensuring great experiences for our growing communities centered around our world-renowned brands, including fan-favorites like Apex, Battlefield, EA SPORTS FC, Madden NFL and The Sims, just to name a few. We're a multi-functional group, with world-class expertise building fandoms, driving interactive storytelling, and positioning our franchises at the center of the broader entertainment ecosystem. We inspire, connect, and engage fans through culturally relevant content, intentionally architected journeys across channels, and meaningful fan care. Our goal is to provide valuable, easy experiences that fans love – in our games, around our games, and through innovative adjacent experiences to grow and enrich how fans experience EA as we shape the future of entertainment.

To empower more players and fans in new and amazing ways, we need more innovators to join our world-class team. The future of entertainment is interactive, and you can help lead that future, by growing and enriching how hundreds of millions of people (and counting) find joy and belonging, forge friendships, and celebrate their lived experiences through the work we do every single day, together.

About the Role

We are seeking a Senior AI/ML Research Scientist to advance the next generation of Generative AI capabilities across creative content generation and foundation models. This role will focus on developing, adapting, and optimizing state-of-the-art AI models, including diffusion models, LLMs, multimodal architectures, and Graph Neural Networks (GNNs).

You will drive research and development efforts and model architectures that enable scalable, high-quality content generation. The ideal candidate combines deep scientific expertise with hands-on experience building and training large-scale machine learning systems.

Working closely with cross-functional teams of engineers, product leaders, and domain experts, you will help define the organization's AI strategy and deliver breakthrough capabilities that leverage advances in GenAI and Deep Learning.

What You'll Do

• Lead research and development of state-of-the-art generative AI systems, including diffusion models, latent diffusion architectures, and multimodal foundation models.

• Collaborate with stakeholders to understand business needs and translate them into model requirements. Advise on what is possible and the impact that the models can drive.

• Design, train, fine-tune, and optimize large-scale AI models for creative content generation.

• Develop and apply parameter-efficient adaptation techniques, including LoRA, adapters, prompt tuning, and related methods for foundation model customization.

• Advance internal LLM capabilities through model training, fine-tuning, evaluation, alignment, and optimization.

• Research and implement Graph Neural Network (GNN) architectures to model complex relationships, knowledge graphs, recommendation systems, and structured data representations.

• Evaluate emerging research and rapidly prototype innovative approaches from leading conferences and publications.

• Collaborate with engineering teams to transition research innovations into scalable production systems.

Qualifications

Minimum Qualifications

• PhD or Master's in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.

• 7+ years of experience conducting advanced machine learning research and development with a track record of translating cutting-edge research into impactful products or platforms.

• Strong publication record in leading AI conferences or journals (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, KDD, AAAI, or equivalent).

• Deep expertise in deep learning fundamentals, optimization, representation learning, and neural network architectures.

• Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or TensorFlow.

• Experience in training and evaluating large-scale machine learning models on distributed compute infrastructure and GPU-accelerated computing environments

Preferred Qualifications

• Expertise in developing and training diffusion models, latent diffusion models, or related generative architectures.

• Experience fine-tuning foundation models using LoRA, QLoRA, adapters, PEFT techniques, RLHF, DPO, or similar approaches.

• Experience developing generative AI systems for image, video, audio, 3D, or other creative content domains.