AI Systems, Language & Reasoning Models

🔥 39 minutes ago

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Artificial Intelligence

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Unconventional AI

WebsiteLinkedInAll Job Openings

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.

📋 Description

• Drive the development of foundational language and reasoning models that fundamentally leverage the dynamics of our novel silicon. • Map the behaviors of modern language models directly onto the physics of our hardware. • Rethink standard sequence modeling to exploit the continuous-time dynamics of silicon, moving away from layers of inefficient digital abstraction. • Establish the training recipes, loss functions, and evaluation metrics needed to reach the frontier of language comprehension, logical reasoning, and generation speed while maintaining the massive energy efficiency of our platform. • Collaborate with hardware designers and theorists, and system builders to co-design the model architecture alongside the underlying physical compute primitives.

🎯 Requirements

• 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, hands-on expertise in the theory, architecture, and training of modern foundation models (transformers, SSMs, text diffusion/flow, etc.). • Hands-on, battle-tested experience dealing with model scaling. You have successfully designed and executed full-scale, distributed training runs for large language or reasoning models, managing the complexities of massive compute clusters. • You are fluent in modern deep learning frameworks (PyTorch or JAX) and have a proven track record of writing clean, scalable training code for large language models. • As a bonus, you may have experience working with hardware-in-the-loop training, mixed-signal hardware, quantization, or physics-informed neural networks

🏖️ Benefits

• A comprehensive package including best-in-class health benefits • 401k matching • Truly unlimited PTO • Complimentary meals in our Palo Alto office