Summary
David Rose is a Staff Machine Learning Engineer in New York with 11 years of experience building production AI systems, from low-latency recommender engines to modern generative AI and agent platforms. At Zeta Global he’s led the architecture and deployment of multi-agent systems, long-running LLM workflows, multimodal fine-tuning, and the platform patterns needed to ship resilient AI features into enterprise products. He combines hands-on work in LLM fine-tuning, RAG, tool-using agents, and real-time inference with pragmatic engineering—Kubernetes, AWS, observability, and deployment pipelines—to move projects beyond demos into durable, operable systems. His background in economics and early work on automated financial reporting and bid-optimization models gives him a rare mix of product-focused ML engineering and operational rigor. He’s active on GitHub building Python-first ML tooling and emphasizes reproducible evaluation and rollout strategies for production models.
11 years of coding experience
6 years of employment as a software developer
Machine Learning, Machine Learning at Udacity
Economics & Finance, Economics & Finance at Samford University