Software Engineer, On Device Machine Learning
Posted Updated 10
views
Summary
The Software Engineer will work on EA's Frostbite engine to develop and integrate runtime machine learning solutions for games. The role involves optimizing ML models for on-device performance using C++ and collaborating with cross-functional teams to deploy these models into production.
We are looking for a Software Engineer with expertise in developing and integrating runtime machine learning solutions to join our team. You will report to a Leader of Engine Development and collaborate with game teams, central technology engineers, and researchers to bring ML models into the hands of our players by deploying them directly within EA games.
Responsibilities:
- Collaborate with game teams and ML experts to understand model architectures, training workflows, and deployment constraints, then recommend improvements that enable efficient real-time in-game inference.
- Extend our existing ML runtime technologies, or build new ones, to run models efficiently across a variety of devices, with clean, well-tested code.
- Translate ML concepts, trade-offs, risks, and technical constraints into clear language for different audiences.
- Stay current with advancements in Deep Learning, Reinforcement Learning, Generative AI, and related fields, identifying relevant research and proposing ways to apply it.
- Contribute to Frostbite's and EA's ML/AI communities by sharing knowledge.
Qualifications:
- 5+ years of hands-on software engineering experience with C++, including a solid understanding of multithreading, memory and performance optimization.
- Familiarity with training models using ML frameworks such as PyTorch or TensorFlow.
- Practical experience with software engineering fundamentals, including version control, code reviews, documentation, automated testing, coding standards, CI/CD, issue tracking, and Agile development.
- A BSc or MSc in Computer Science, Engineering, Mathematics, or equivalent professional experience.
Nice to have:
- Experience building and shipping ML systems in production environments.
- Familiarity with ONNX or other intermediate representations for ML model graphs.
- Experience profiling and optimizing real-time software.
- GPU programming experience, such as writing compute shaders in HLSL or similar languages.
- Experience with game programming or game engine development.