14 Neurophos Jobs Hiring Near You

Job Summary:
Neurophos is a pioneering company focused on redefining AI computing through innovative optical architecture. They are seeking a Staff Machine Learning Architect to lead the adaptation and optimization of machine learning models for their advanced optical inference engines, bridging the gap between cutting-edge research and practical hardware applications.
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)
Qualifications:
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
Company:
Neurophos develops photonic AI processing technology that focuses on hardware solutions for accelerating artificial intelligence inference. Founded in 2020, the company is headquartered in Austin, USA, with a team of 11-50 employees. The company is currently Early Stage.