AI Systems, Model Optimization

Unconventional AI is focused on redefining computing to solve the fundamental energy limitations of AI. The role of Member of Technical Staff, AI Systems, Model Optimization involves developing training techniques and optimization strategies to ensure AI models run efficiently on novel compute substrates.

Responsibilities:

• Develop rigorous performance models to evaluate compute, memory, and energy trade-offs. Track pareto-optimality across models and hardware configurations
• Drive the partitioning and mapping of complex AI models down to hardware. Apply and invent advanced optimization strategies from first principles, including custom quantization schemes, sparsity/pruning, and distillation to fit the physical constraints of our substrates
• Develop and apply Quantization-Aware Training (QAT), noise-aware training, and sparsification techniques to adapt models to the physical constraints of our analog compute substrates, including memory footprint, connectivity, precision, and noise
• Develop and optimize kernels using low-level programming models like CUDA, Triton, or CUTLASS. Profile and debug complex ML codebases to resolve performance bottlenecks (training and inference)
• Act as a translator between AI model architects and hardware/infrastructure engineering teams, converting model requirements into concrete specifications and codifying learnings for tapeouts