ML Senior Software Engineer - GPU/CPU
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Frostbite is EA's proprietary game engine. Its versatile tools and cutting-edge technology power creation on some of the world's most iconic games, including titles from EA SPORTS and Battlefield. By joining Frostbite, you'll be at the forefront of game engine innovation, collaborating with top-tier developers to push the boundaries of interactive realism and immersive gameplay.
We are looking for a Senior Software Engineer with expertise in software optimization for gaming consoles and CPU/GPU architectures to join our Machine Learning team. You'll report to a Leader of Engine Development and collaborate with both game and central technology engineers and researchers to bring ML models into the hands of our players by deploying them directly into EA's games.
Responsibilities:
- Design, build, and maintain robust end-to-end solutions for running machine learning models efficiently on a variety of devices.
- Partner with ML experts across EA to help adopt and scale new models and architectures optimized for on-device performance.
- Integrate ML solutions into our proprietary tools and game runtime environments on consoles, PCs, and mobile devices.
- Write clean, well-documented, and well-tested code that integrates smoothly with existing systems.
- Keep up to date with the latest advancements in Deep Learning, Reinforcement Learning, Generative AI, and related fields, through continuous learning and by attending internal and external conferences.
- Be an enthusiastic contributor to Frostbite's and EA's ML/AI communities - sharing your knowledge, collaborating with partners, and mentoring teammates along the way.
Qualifications:
- 7+ years of hands-on software engineering experience with C++, including expertise in multithreading and low-level/near-hardware optimizations.
- Good knowledge of GPU programming.
- Proficient in debugging, profiling, and optimizing real-time software.
Nice to have:
- Experience with implementing software tools to optimize code.
- Knowledge of ML frameworks such as PyTorch or TensorFlow.
- Knowledge of the ONNX format.
- Experience building, debugging, and shipping end-to-end ML systems in real-world production environments.