
11 - 50 employees
🤖 Artificial Intelligence
🔧 Hardware
⚡ Energy
Artificial Intelligence • Hardware • Energy
Unconventional AI is rethinking the foundations of a computer to optimize energy efficiency for AI. Founded by experts in AI systems, analog circuits, computing theory, and neuroscience, the company aims to bring biology-scale efficiency to artificial intelligence by redesigning computing architecture and hardware to reduce power consumption for AI workloads.
🕒 July 27
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11 - 50 employees
🤖 Artificial Intelligence
🔧 Hardware
⚡ Energy
Artificial Intelligence • Hardware • Energy
Unconventional AI is rethinking the foundations of a computer to optimize energy efficiency for AI. Founded by experts in AI systems, analog circuits, computing theory, and neuroscience, the company aims to bring biology-scale efficiency to artificial intelligence by redesigning computing architecture and hardware to reduce power consumption for AI workloads.
• Develop the path from model architecture to physical silicon. • Develop the training techniques, optimization strategies, and infrastructure required to make AI models run efficiently on novel compute substrates. • Develop rigorous performance models to evaluate compute, memory, and energy trade-offs. • Drive the partitioning and mapping of complex AI models down to hardware. • Develop and apply Quantization-Aware Training (QAT), noise-aware training, and sparsification techniques. • Develop and optimize kernels using low-level programming models like CUDA, Triton, or CUTLASS. • Act as a translator between AI model architects and hardware/infrastructure engineering teams.
• An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math. • Deep, practical understanding of the modern AI/ML stack and optimized compilation and execution of algorithms on modern GPU systems. • Proven experience in profiling, identifying, and resolving performance bottlenecks in complex ML codebases. • Demonstrated ability to map state-of-the-art AI model architectures (e.g., Transformers, Mixture of Experts, diffusion models) to system performance implications. • Deep experience with PyTorch, including its internals, torch.compile, and distributed data parallel (DDP) / fully sharded data parallel (FSDP) libraries.
• A comprehensive package including best-in-class health benefits • 401k matching • Truly unlimited PTO • Complimentary meals in our Palo Alto office
Apply Now🕒 July 27
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