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Jobs · Engineering · California

Senior ML Engineer, Optimization

Neurophos · Sunnyvale, CA · 1 mo ago
On-siteEngineering$110/hrFull-time

About Neurophos

Neurophos is rethinking the fundamental architecture of inference compute to overcome the power wall facing AI and data centers. By replacing traditional electronic circuits with silicon photonics and programmable metasurfaces, the company performs matrix multiplications at the speed of light using optical cells 10,000x smaller than conventional photonic components. This approach delivers up to 100x energy efficiency while scaling performance for large-scale AI inference. Neurophos has raised a $110M Series A led by Gates Frontier, with participation from M12 (Microsoft’s Venture Fund), Carbon Direct Capital, Aramco Ventures, Bosch Ventures, Tectonic Ventures, Space Capital, and others. The team is based in Austin, TX or Sunnyvale, CA.

Position Overview

We are seeking an experienced machine learning engineer to develop advanced post-training quantization methods for large language models (LLMs), diffusion models, and other ML applications tailored to our optical inference engines. This role bridges cutting-edge ML research with novel hardware, ensuring customers can deploy AI workloads efficiently on Neurophos hardware.

Responsibilities

  • Develop and execute hardware-aware post-training methods for full model quantization.
  • Investigate preconditioning and formulate quantization as non-convex, discrete, constrained, or second-order optimization, and develop practical solutions.
  • Refine Neurophos’s quantization strategy.
  • Design controlled numerical experiments to assess improvements and secondary effects from analog processing hardware.
  • Build research-quality implementations and reproducible experiment harnesses for testing candidate methods.
  • Adapt models from open-source repositories and customer private models across formats including PyTorch, Triton, JAX, and emerging frameworks.
  • Design and execute re-quantization, retraining, and other model adaptation techniques to minimize accuracy loss during precision reduction.
  • Optimize GEMM operations for high-throughput execution.
  • Collaborate with hardware, software, and architecture teams to co-optimize model architectures for optical compute characteristics.
  • Publish research papers on novel optimization techniques and methodologies with appropriate IP protection.

Qualifications

  • 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 engineering, including 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.
  • Hands-on experience with transformer architectures, LLMs, and diffusion models.
  • Experience designing controlled numerical experiments and distinguishing algorithmic improvements from calibration or benchmark artifacts.
  • Strong written communication and research collaboration skills.

Preferred Skills

  • Experience with low-precision inference optimization (INT8, FP8, or lower).
  • Background in analog or optical computing architectures.
  • Knowledge of in-memory computing paradigms and matrix-vector multiplication acceleration.
  • Knowledge of randomized numerical linear algebra, sketching, or structured transforms.
  • Publications in quantization, optimization, numerical linear algebra, model compression, or efficient ML.
  • Experience with vector quantization, lattice methods, learned codebooks, or rate-distortion ideas.
  • Experience with large-scale batch inference optimization.
  • Familiarity with prefill versus decode optimization strategies in LLM inference.
  • Experience conducting experiments on models large enough to expose scaling and generalization problems.

Benefits

  • 100% coverage of base health plan premiums for you and your dependents, plus HSA contributions.
  • Unlimited PTO.
  • 401(k) matching and stock option opportunities.
  • Full suite of voluntary benefits, including Dental, Vision, Life, Hospital, Critical Illness, and Accident insurance.
  • Personalized benefits: choose plans that fit your life and take cash back for those that don’t.

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