
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.
🔥 3 hours ago
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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.
• 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.
• 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. • Veteran of the modern ML software stack. Demonstrated ability to map state-of-the-art AI model architectures (e.g., transformers, Mixture of Experts, diffusion models) to system performance implication. Deep expertise in how models are partitioned across a cluster, with a mastery of communication primitives, and parallelism strategies. • Proven track record of implementing, debugging, and maintaining production-grade training frameworks—such as Megatron-LM, DeepSpeed, Ray, PyTorch Lightning—turning raw compute into a reliable model-building factory.
• best-in-class health benefits • 401k matching • truly unlimited PTO • complimentary meals in our Palo Alto office
Apply Now🔥 4 hours ago
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