Summary
Chris Hayduk is a Forward Deployed Engineer specializing in life sciences at OpenAI with 11 years of experience building and deploying AI systems for drug discovery, clinical development, and related biomedical workflows. He combines deep applied ML expertise—from GNNs and LLMs to distributed training with FSDP/DeepSpeed—with hands-on production experience integrating models like AlphaFold, ESMFold, and DiffDock into scalable cloud architectures. Previously at Meta he improved ads ranking using graph representation learning, and at Deloitte he led Atlas AI, architecting causal knowledge graphs, RAG pipelines, and full-stack deployments including React/Node backends. Trained in mathematics and applied statistics and currently pursuing an MS in Computer Science (AI) at Georgia Tech, he bridges rigorous quantitative foundations with pragmatic engineering. Notably, he has retrained and fine-tuned 15B+ parameter models and operationalized large-scale biomedical model stacks, reflecting a rare mix of research, systems, and product delivery in life sciences AI.
11 years of coding experience
6 years of employment as a software developer
Master of Science - MS Mathematics, Master of Science - MS Mathematics at The City College of New York
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Georgia Institute of Technology
Bachelor of Science (B.S.) Mathematics and Computer Science, Bachelor of Science (B.S.) Mathematics and Computer Science at Fordham University
Master of Science - MS Applied Statistics, Master of Science - MS Applied Statistics at Fordham Gabelli School of Business
English, Spanish