Senior/Staff Applied Scientist, Numerical Optimization & Quantization

What you'll do

• Develop hardware-aware post-training quantization and model-adaptation methods, including re-quantization and retraining.
• Design numerical experiments and reproducible implementations; optimize GEMM operations and evaluate model performance on optical hardware.
• Collaborate across hardware, software, and architecture teams to co-optimize models, and publish research with appropriate IP protection.

What they ask for

• PhD or equivalent research experience in a relevant field.
• 5+ years in machine learning, including at least 3 years focused on model optimization and deployment.
• Research or advanced engineering experience in quantization, model compression, numerical optimization, or efficient inference; strong numerical linear algebra knowledge.
• Proficiency in PyTorch; experience with transformer architectures, LLMs, diffusion models, and optimization methods.

Nice to have

• Experience with low-precision inference (INT8, FP8, or lower) or analog/optical computing.
• Knowledge of randomized numerical linear algebra, vector quantization, or large-scale inference optimization.

Pay

$170K – $240K (base salary)

Tools

PyTorch, Triton, JAX, TensorFlow, INT8, FP8

AI focus

Develop quantization and optimization methods for LLMs, diffusion models, and other ML applications deployed on optical inference hardware.