Sam Daneshvar is a Senior Machine Learning Engineer with 9 years of experience building production-grade ML and LLM systems, currently architecting agentic multi-agent workflows and RAG pipelines at Distro (YC S24). He combines research pedigree (CLEF 2018 SOTA with 90+ citations) with hands-on deployment expertise—optimizing GPT-4o routing, caching, and JSON outputs to cut latency 50% and costs 35% at scale. Sam has delivered real-time NLP pipelines processing 1M+ tweets/day, boosted retrieval and sentiment AUC-ROC through FAISS-backed RAG, and improved forecasting accuracy using ARIMA/Prophet and PyTorch on SageMaker. He’s pragmatic about operational trade-offs, having reduced infra overhead via Kubernetes/EKS and shipping LLM evaluation frameworks that run on 50k+ monthly queries. Beyond algorithms, he mentors engineers in experiment tracking and deployment best practices, and favors cost-efficient, low-latency architectures that bridge research and product impact. Based in San Francisco, he blends deep NLP/LLM fluency with cloud-native engineering to turn complex AI use cases into reliable production services.
9 years of coding experience
2 years of employment as a software developer
Master of Science (MSc) Computer Science, Master of Science (MSc) Computer Science at University of Ottawa
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