# Senior/Staff Applied Scientist, Numerical Optimization & Quantization at Neurophos

- Company: Neurophos
- What the company does: Neurophos develops photonic AI processing technology that focuses on hardware solutions for accelerating artificial intelligence inference by replacing traditional electronic compute elements with Optical Processing Units (OPUs). Backed by Purple Arch Ventures.
- Company website: https://neurophos.com
- Type: Startups (AI role)
- Level: Senior
- Location: Austin, Texas
- Work setup: On-site
- Pay: $170K to $240K base salary per year (USD)
- Posted: 2026-08-21
- Apply by: 2026-10-14
- Apply: https://jobs.ashbyhq.com/neurophos/c8275d25-5852-48dd-8d51-c94ec382f010
- Page: https://www.1752.vc/careers/jobs/neurophos-senior-staff-applied-scientist-numerical-optimization-and-quantization/

## About the role

We are seeking an experienced machine learning scientist to develop advanced post-training quantization methods for large language models (LLMs), diffusion models, and other ML applications for our revolutionary optical inference engines. This role is critical to demonstrating the full potential of our metamaterial-based optical processing units (OPUs) by adapting state-of-the-art AI models to leverage our ultra-high-throughput, low-precision compute architecture.

## What they're looking for

- PhD, or equivalent research experience, in machine learning, applied mathematics, optimization, numerical analysis, computer science, or a closely related field
- 5+ years of experience in machine learning, with at least 3 years focused on model optimization and deployment
- Research or advanced engineering experience in neural network quantization, model compression, numerical optimization, or efficient inference
- Strong knowledge of numerical linear algebra, including matrix factorizations, conditioning, covariance estimation, and iterative methods
- Experience with one or more of non-convex optimization, discrete optimization, manifold optimization, second-order methods, or constrained optimization
- Strong proficiency in PyTorch and familiarity with other ML frameworks, including JAX, Triton, and TensorFlow

Tags: Mathematics