Unconventional AI
AI Systems, Language & Reasoning Models
Mountain View, CA I US Remote +2 · Remote · Full-time · Posted Apr 14, 2026
- Location
- Mountain View, CA I US Remote
Mountain View
Remote - Education
- MS/PhD or equivalent research/project experience in a quantitative field, such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math.
About the job
Develop and scale language and reasoning models designed to run on Unconventional AI’s novel silicon, co-designing model architectures with hardware to improve performance and energy efficiency.
What you'll do
- Design, train, and scale language and reasoning architectures for unconventional compute.
- Develop training recipes, loss functions, and evaluation metrics for language understanding, reasoning, and generation.
- Collaborate with hardware designers, theorists, and systems builders on model and compute-primitives codesign.
What they ask for
- Deep hands-on expertise in modern foundation-model theory, architecture, and training.
- Experience designing and executing full-scale distributed training runs for large language or reasoning models on compute clusters.
- Fluency in PyTorch or JAX and a record of writing clean, scalable LLM training code.
Nice to have
- Experience with hardware-in-the-loop training, mixed-signal hardware, quantization, or physics-informed neural networks.
Tools
transformers, state space models, diffusion/flow models, deep equilibrium models, PyTorch, JAX
AI focus
Build foundational language and reasoning models that leverage the dynamics of novel silicon, including physics-informed architectures, large-scale training, and evaluation.
Model-written summary · the original posting is authoritative