Senior Applied Scientist — Optical AI Quantization

Neurophos; Austin (TX); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Health plan premiums coverage

Unlimited PTO

401(k) matching

Stock options

Voluntary benefits

Job summary

Neurophos seeks an experienced ML scientist to develop post-training quantization methods for LLMs, diffusion models, and other ML workloads on our optical inference engines. You will bridge ML research with Neurophos hardware capabilities, enabling seamless deployment of AI workloads.

The role requires strong background in quantization, numerical optimization, and familiarity with PyTorch/JAX/Triton, plus collaboration across hardware and software teams.

Qualifications

• PhD or equivalent research degree in ML, optimization, or related field.
• 5+ years of ML experience with model optimization and deployment.
• Experience in neural network quantization, model compression, or efficient inference.

Responsibilities

• Develop hardware-aware post-training methods for full model quantization.
• Investigate preconditioning and formulate optimization approaches for quantization.
• Refine quantization strategy and design controlled experiments for optical compute hardware.
• Collaborate with hardware, software, and architecture teams to co-optimize models.

Skills

PyTorch proficiency

Transformer models / LLMs

Quantization / model optimization

Numerical linear algebra

Non-convex optimization

Written communication

Education

PhD or equivalent

Tools

PyTorch

JAX

Triton

TensorFlow

Job description

Neurophos seeks an experienced ML scientist to develop post-training quantization methods for LLMs, diffusion models, and other ML workloads on our optical inference engines. You will bridge ML research with Neurophos hardware capabilities, enabling seamless deployment of AI workloads.

The role requires strong background in quantization, numerical optimization, and familiarity with PyTorch/JAX/Triton, plus collaboration across hardware and software teams.