Summary
Ralph Bird is a Principal Machine Learning Engineer with 11 years of experience building production-grade ML and generative AI systems that meet real-world constraints like latency, cost ceilings, FedRAMP, and operational reliability. At PagerDuty he has led cross-functional, architecture-level initiatives—shaping Python standards, deploying Azure OpenAI and AWS Bedrock integrations, and turning complex requirements into scalable, maintainable systems. Previously he founded Reachdesk’s data science function and shipped a production recommendation system, and earlier work ranges from building mobile digital therapeutics and on-device neural nets to leading large astrophysics software projects at UCLA. He blends rigorous academic training (PhD in Astrophysics, Cambridge MSci) with hands-on engineering, and is known for focusing on solutions that survive production pressures rather than just experimental accuracy. An award-winning collaborator, Ralph brings both domain depth and an uncommon track record of moving research-grade models and infrastructure into reliable enterprise use.
11 years of coding experience
10 years of employment as a software developer
MSc, Physics and Technology of Nuclear Reactors, Distinction, MSc, Physics and Technology of Nuclear Reactors, Distinction at The University of Birmingham
MSci, Natural Sciences (Physics), 2.i, MSci, Natural Sciences (Physics), 2.i at University of Cambridge
Doctor of Philosophy (PhD), Astrophysics, Doctor of Philosophy (PhD), Astrophysics at University College Dublin
English