# Senior Modeling Architect, Performance Benchmarking 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
- Level: Senior
- Location: Austin, Texas
- Work setup: On-site
- Pay: $210K to $250K base salary per year (USD)
- Posted: 2026-09-04
- Apply by: 2026-10-19
- Apply: https://jobs.ashbyhq.com/neurophos/e97c4a7a-c36e-4d29-8c8c-2baad33cd04c
- Page: https://www.1752.vc/careers/jobs/neurophos-senior-modeling-architect-performance-benchmarking/

## About the role

We are seeking a performance engineer to own the benchmarking numbers behind the T100 optical inference accelerator. Architecture and product decisions here are made on measured performance and energy, and this role produces those figures for the same workloads at every level of fidelity we use: roofline and limiter analysis, architecture performance models, in-house RTL simulation, and measured runs on competing GPUs and accelerators.

## What they're looking for

- BS or MS in Computer Engineering, Electrical Engineering, Computer Science, or equivalent practical experience
- 5+ years of experience in GPU performance engineering, accelerator benchmarking, HPC performance measurement, or ML systems measurement
- Track record of building or operating benchmark harnesses that produced measured results on real GPUs or accelerators, including turning a Hugging Face model card, paper, or application description into a runnable benchmark
- Hands-on experience with roofline analysis, limiter analysis, or analytical performance modeling
- GPU performance analysis with NVIDIA Nsight Systems and Nsight Compute, or an equivalent profiler, covering HBM-bound versus compute-bound analysis, precision (FP16, BF16, FP8, INT8), and batching
- Working knowledge of LLM inference stacks such as Hugging Face, vLLM, SGLang, or TensorRT-LLM, including prefill versus decode, continuous batching, and MoE

Tags: Engineering