Neurophos · 1 day ago
Senior Machine Learning Architect
Neurophos is revolutionizing AI computation with its metamaterial-based optical computing platform. They are seeking an experienced machine learning architect to lead the porting and optimization of large language models and other ML applications to their optical inference engines, bridging the gap between ML research and hardware capabilities.
Responsibilities
Lead the porting of LLM applications, diffusion models, and visual ML applications to Neurophos optical inference engines
Adapt models from diverse sources, including GitHub, Hugging Face, other open-source repositories, and customer private models
Work with models in various formats, including PyTorch, Triton, JAX, and emerging frameworks
Develop and implement quantization strategies to migrate models from higher precision formats (FP8, INT8, and above) to our optimized 4-bit precision (FP4/INT4) for weights and activations
Design and execute re-quantization, retraining, and other model adaptation techniques to minimize accuracy loss during precision reduction
Create or integrate third-party tools and workflows for efficient model porting and optimization
Optimize GEMM operations for high-throughput execution
Develop benchmarking methodologies to measure and validate model quality post-porting, including perplexity metrics and other quality indicators
Collaborate with hardware and software teams to co-optimize model architectures for optical compute characteristics
Publish research papers on novel optimization techniques and methodologies (with appropriate IP protection)
Qualification
Required
MS or PhD in Computer Science, Data Science, Machine Learning, Mathematics, or related field
7+ years of experience in machine learning engineering with at least 3 years focused on model optimization and deployment
Deep expertise in neural network quantization techniques, including post-training quantization (PTQ) and quantization-aware training (QAT)
Strong proficiency in PyTorch and familiarity with other ML frameworks (JAX, Triton, TensorFlow)
Hands-on experience with transformer architectures, LLMs, and diffusion models
Experience with low-precision inference optimization (INT8, FP8, or lower)
Strong understanding of GEMM operations and linear algebra optimizations for deep learning
Experience with model evaluation metrics, including perplexity, accuracy, and benchmark suites
Track record of successfully deploying ML models on specialized hardware accelerators
Excellent communication skills with the ability to collaborate across hardware and software teams
Preferred
Experience with sub-8-bit quantization (INT4, FP4) and mixed-precision inference
Familiarity with Hugging Face Transformers library and model hub ecosystem
Experience with ONNX, TensorRT, or other model optimization frameworks
Background in analog or optical computing architectures
Knowledge of in-memory computing paradigms and matrix-vector multiplication acceleration
Published research in model compression, quantization, or efficient inference
Experience with large-scale batch inference optimization
Familiarity with prefill vs. decode optimization strategies in LLM inference
Benefits
Competitive compensation, including salary and equity options.
Opportunities for career growth and future team leadership.
Access to cutting-edge technology and state-of-the-art facilities.
Opportunity to publish research and contribute to the field of efficient AI inference.
Company
Neurophos
Exaflop optical AI acceleration.
H1B Sponsorship
Neurophos has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2023 (1)
Funding
Current Stage
Early StageTotal Funding
$16.1MKey Investors
Gates Frontier FundMetaVC Partners
2025-12-08Series Unknown· $1.9M
2023-12-14Seed· $7.2M
2023-10-03Seed· $7M
Recent News
2025-09-24
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2025-08-21
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