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Unconventional AI

Analog computing substrate for energy-efficient AI

AI Theory & Systems - Algorithms & Models

AI ResearchConfirmed live in the last 24 hours
Full-timeMountain View, CA, USANo salary listed

About the Job

Requirements

  • An MS, PhD, or equivalent research or project experience in a quantitative field such as artificial intelligence or machine learning, computer science, physics, electrical engineering, or applied mathematics.
  • Deep expertise in the theory, training, and empirical analysis of modern machine learning model architectures.
  • Demonstrated ability to design, implement, and scale models across tasks and modalities.
  • Strong understanding of conventional architectures, including Transformers, Mixture of Experts, and diffusion models, and continuous-time or implicit architectures, including Neural ODEs, Deep Equilibrium Models, and flow-based generative models, including their computational properties and trade-offs.
  • Deep experience with PyTorch or JAX, including implementation of custom model components and training loops.

Responsibilities

  • Design and scale model architectures combining novel computational primitives with conventional neural network components to achieve competitive capability across modalities.
  • Build scaled, end-to-end architectures for image generation, language and reasoning, video, and world models that function optimally on novel compute substrates.
  • Co-design and evaluate next-generation AI models, including diffusion, flow, energy-based models, Neural ODEs, and Deep Equilibrium Models.
  • Collaborate across the team to combine, modify, and implement core modeling components, including attention, normalization, Mixture of Experts, feed-forward networks, and unconventional components, and ensure they function optimally across novel compute substrates.
  • Establish and test scaling laws specific to novel hardware.
  • Develop and refine methodologies and strategies, such as muP, to push model capability toward the frontier of conventional and newly enabled capability.
  • Investigate novel architectures across image generation, video, language and reasoning, and world models.
  • Develop general learning frameworks, such as flow matching, across these domains.
  • Translate between teams so architectural decisions account for fundamental-science insights and hardware constraints.
  • Discuss algorithmic trade-offs with theorists and convert model requirements into concrete specifications for systems and hardware teams.

Desired Qualifications

  • Experience with numerical methods for differentiating through continuous-time systems, such as backpropagation through differential equations, adjoint methods, and fixed-point solvers.
  • A forward-looking perspective on co-designing model architectures for unconventional computing paradigms that map closely to the physics of underlying systems.

About the Company

Unconventional AI

Analog computing substrate for energy-efficient AI

Unconventional AI designs analog AI hardware and silicon substrates that run neural networks directly in hardware. Its chips use analog circuits that mimic biological neuron dynamics to process AI workloads in the physics of silicon, reducing energy use compared with digital GPUs. It differentiates itself by pursuing an in-hardware, neuromorphic-style substrate rather than optimizing existing GPUs, supported by prominent venture funding. Its goal is to counter the AI energy bottleneck and enable scalable AI in data centers with biology-scale efficiency.

Company size
51-200
Company stage
Seed
Total funding
$475M
Headquarters
San Diego, CA
Founded
2025

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