AI Systems, Training

Unconventional AI is pioneering a new foundation for AI computing that is significantly more efficient. The role focuses on developing a next-generation ML model training platform and involves designing training systems, optimizing performance, and collaborating across teams to push the boundaries of AI technology.

Responsibilities:

• Build and maintain highly optimized, model-specific training stacks specifically tuned for state-of-the-art generative vision, language, and world models
• Design and scale multi-node distributed training systems, implementing elastic sharding and robust data streaming pipelines for fast, large-scale iteration
• Implement and robust model checkpointing and recovery mechanisms
• Develop and optimize kernels using low-level programming models like CUDA and Triton
• Design rigorous benchmarking suites to track Model Flops Utilization (MFU), memory bandwidth, and convergence stability
• Act as a translator, discussing algorithmic trade-offs with theorists and converting model requirements into concrete specifications for infrastructure and hardware engineering teams