Electronic Arts

About the Role

We’re EA, one of the world’s largest video game publishers. Our central development teams power every EA game you know and love. From localization and culturalization to testing, art, rigging, animation, capture, and cinematics we are the backbone that helps our studios bring immersive worlds to life.

As part of EA’s AI Transformation, we’re reimagining how games are created, tested, and localized by deeply integrating generative AI and agentic systems into every part of our central development pipeline.

We are seeking a Machine Learning Engineer on the team, to design, train, and deploy large-scale AI models that drive innovation in human motion generation, real-time performance capture, and multimodal understanding. You will work at the intersection of research and production—building intelligent systems that enable more natural, dynamic, and scalable character and motion technologies across EA titles.

Key Responsibilities

• Lead the design and implementation of deep learning models for motion capture, pose estimation, and generative motion synthesis.
• Develop and optimize transformer-based architectures and multimodal AI systems for real-time inference and large-scale data processing.
• Collaborate with research, animation, and engineering teams to translate experimental models into production-ready pipelines.
• Develop scalable training and deployment systems using frameworks such as PyTorch, TensorRT, and gRPC.
• Work with cross-functional partners to prototype, benchmark, and validate new models and algorithms for animation, capture, and simulation.
• Implement best practices for data curation, synthetic dataset generation, and model evaluation.
• Drive experimentation and innovation through close collaboration with academic institutions and industry partners.

Qualifications

• PhD or Master’s degree in Computer Science, Engineering, or a related field, with focus on Machine Learning, Computer Vision, or Applied AI.
• 5+ years of professional experience developing and deploying machine learning systems in production.
• Proven expertise in deep learning for human motion capture, pose estimation, or 3D vision.
• Proficiency in Python, PyTorch, TensorFlow, and deep learning optimization toolkits (e.g., TensorRT).
• Strong understanding of transformer architectures, LLMs, and generative adversarial networks (GANs).
• Demonstrated ability to lead R&D initiatives, mentor technical staff, and deliver results across multidisciplinary teams.
   
• Excellent problem-solving, communication, and documentation skills.