AI Theory & Systems, Algorithms & Models

Co-design Neural ODEs, Deep Equilibrium Models, and diffusion architectures mapped directly to semiconductor device physics.

Originally posted April 3, 2026 by the employer. Relisted by the employer on November 14, 2026.

About the role

As a Member of Technical Staff, AI Systems, Algorithms & Models, you will design and scale model architectures that combine novel computational primitives with conventional neural network components across modalities. You will build scaled, end-to-end architectures for image generation, language & reasoning, video, and world models optimized for novel compute substrates.

What you'll do

• Co-design and evaluate AI models, including diffusion, flow, energy-based models, Neural ODEs, and Deep Equilibrium Models.
• Combine, modify, and implement core modeling components, including attention, normalization, Mixture-of-Experts, and FFNs alongside unconventional primitives.
• Establish and test scaling laws specific to novel hardware, developing strategies such as muP to expand model capability.
• Investigate novel architectures across modalities including image generation, video, language & reasoning, and world models using general learning frameworks like flow matching.
• Translate architectural decisions between theoretical science insights and physical hardware constraints, converting model requirements into concrete specifications for systems and hardware teams.

What you'll need

• MS, PhD, or equivalent research/project experience in AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math.
• Expertise in the theory, training, and empirical analysis of modern ML model architectures, with demonstrated experience designing, implementing, and scaling models across tasks and modalities.
• Understanding of conventional architectures (Transformers, Mixture of Experts, diffusion models) and continuous-time or implicit architectures (Neural ODEs, Deep Equilibrium Models, flow-based generative models), including their computational properties and trade-offs.
• Deep experience with PyTorch or JAX, including the implementation of custom model components and training loops.

Nice to have

• Experience with numerical methods for differentiating through continuous-time systems (e.g., backpropagation through differential equations, adjoint methods, fixed-point solvers).
• Perspective on co-designing model architectures for unconventional computing paradigms mapped to the physics of underlying systems.

Skills: AI algorithms, ML models, AI theory, systems

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