AI Engineer

Eidon AI - Austin, TX

Full-time

Austin, TX

About the position

Eidon AI is seeking an AI Engineer to contribute to the development of decentralized AI systems. The role involves designing and deploying AI models and algorithms within a decentralized data environment, collaborating with data engineers, and researching new AI techniques to enhance data availability and distribution. The ideal candidate will be hands-on, innovative, and committed to advancing AI research while ensuring fairness and accessibility for all participants.

Responsibilities

• Design, develop, and deploy AI models and algorithms within a decentralised data environment.
• Collaborate with Data Engineers to ensure optimal data processing and model training pipelines.
• Research and implement state-of-the-art AI techniques to solve complex problems in data availability and distribution.
• Develop AI Agents for data manipulation and advanced RAG systems.
• Work closely with the community to share insights, models, and advancements, fostering an open, collaborative and decentralised AI research environment.

Requirements

• Engineering or Science background (e.g., CS, CE, or EE degree or equivalent experience).
• Strong background in machine learning, deep learning, and AI model development.
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, JAX) and programming languages such as Python.
• Training large neural networks at scale, as well as finetuning a variety of smaller specialised models.
• Creating AI Agents.
• Creating advanced and high performance RAG systems and adapt them in a distributed environment.
• Mastering all data modalities: text, audio, images, video, realtime and real world data etc.
• Profiling, debugging, and optimising (multi-host) GPU utilisation.
• Rapidly implementing the latest state of the art methods from the deep learning literature.
• Inventing new algorithms and methods that bring us closer to developing decentralised AI systems.
• Building distributed training systems for AI models.
• Configuring and troubleshooting hardware and operating-systems for maximum performance.
• Working with JAX/XLA for multi-host training.
• Writing custom CUDA kernels in either C++ or via Triton.
• Digging into third-party source code for debugging and customization.
• Knowledge of blockchain and decentralised technologies that could be utilised in decentralised AI.