About Nathan Frank | Nathan Frank

Nathan Frank

Nathan Frank - ML Platform & Operations Leader

I’m an engineering and AI/ML leader who builds platforms that teams actually use. My work sits at the intersection of ML engineering, platform productization, and people-first leadership, translating between data science, engineering, security, and business stakeholders so teams can ship faster with guardrails.

How I got here

I began in astrophysics, completing an M.S. in Physics & Astronomy at UNC–Chapel Hill after an honors B.S. in Physics (Astrophysics) at UC Santa Cruz. Research taught me to reason from noisy data, model complex systems, and communicate clearly. From there I moved into data science and applied ML, then into sports technology, where I helped build predictive products and the ML platform foundations that supported them.

That experience led me deeper into ML platforms (training, serving, observability, governance) and consulting with a focus on ML engineering and productization across a variety of clients and use cases. Today, at Grainger, I lead Machine Learning Platform & Operations, guiding a manager-of-managers organization to scale responsible AI adoption across many teams.

What I focus on

Platform productization: Databricks + Kubernetes; production-grade model serving; retrieval & search; multi-provider LLM access; data/feature governance.
Enablement & adoption: self-service onboarding, paved-road patterns, internal consulting, training, and best-practice playbooks.
Responsible AI: practical governance and risk controls with security/architecture so the “right way” is the easy way.
Org leadership: hiring and mentoring; developing new managers; building high-retention, diverse teams with clear ownership and healthy feedback loops.

Speaking & media

I speak about enterprise MLOps, AI enablement, and the human side of shipping ML on podcasts, at internal/external events, and with local communities.