San Francisco Bay Area
I build AI systems in production and teach people how to do the same. I’m a hands-on builder and executive leading AI concept development at Crunchyroll (Sony Pictures), a 20M subscriber global business. Before this I was Field CTO at Databricks advising major studios on AI and data strategy, and before that I built and scaled a 40-person ML engineering organization at Activision doing real-time behavioral understanding at global scale. My technical background: M.S. computational chemistry, two ML patents, research with Stanford, NVIDIA, and Caltech. I’ve been building with machine learning since before it was the obvious thing to do. Now I write about agentic AI, what actually happens when you ship AI in production, how to build efficient teams, and the gap between how AI gets talked about and how it actually works. 185k people read along. I partner with tech companies I actually use. If you’re building something in the AI space and want a creator with real technical depth and a real audience, my DMs are open. Fast Company. Stanford. NVIDIA. Caltech. Advocate for women in STEM.
As the Vice President of the Concepts Lab at Crunchyroll (Sony Pictures Entertainment), I own the strategy, portfolio, and execution of early-stage concepts, operating from problem discovery through prototype, pilot, and production handoff. My role combines hands-on building with senior leadership, focused on shipping small, learning fast, and making clear scale or sunset decisions. I work across engineering, product, and business to turn ambiguous opportunities into durable value. - Set the operating model for 0→1 innovation, defining how ideas move from discovery to evidence to adoption. - Maintain a transparent funnel from idea to POC to pilot, with clear success criteria and go/no-go gates. - Provide architectural direction for concepts spanning services, data, AI, and developer tooling. - Move validated concepts into production with delivery teams, ensuring clean handoffs and operational readiness. - Act as a liaison between Crunchyroll engineering and Sony R&D, identifying opportunities to co-develop and accelerate shared innovation.
Rebel Data Science is my independent practice for AI education, content, and brand partnerships. I create technical and accessible content about agentic AI, LLMs, and what it actually looks like to build with AI in production. I partner with tech companies I genuinely use and believe in, working on sponsored content, educational collaborations, and creator partnerships. Audience: 185k on LinkedIn, 4k Substack subscribers. Background: M.S. computational chemistry, two ML patents, 10+ years building and leading ML systems at scale. If you’re an AI or data company looking for a creator with real technical depth and a real audience, reach out.
As Field CTO for Databricks’ gaming and entertainment vertical, I worked as an external technical strategist and AI advisor to some of the largest studios and media companies in the world, including businesses operating at petabyte scale with hundreds of millions of users. The role was equal parts technical architecture, AI strategy, and executive advisory. I worked directly with studio leadership to evaluate early-stage AI and data concepts, define technical direction, and build the infrastructure foundations that make intelligent systems possible at scale. Key focus areas: agentic AI readiness, data platform modernization, personalization and recommendation systems, trust and safety ML, and real-time operational intelligence. I helped studios move from “we have data” to “we have leverage.” The gap between those two things is almost always an architecture problem, a strategy problem, or both.
As a Director for Call of Duty Engineering, I led a team focused on game security. We utilized a suite of technology to make decisions in real-time and strengthened the technology that delivers low-latency concurrent experiences to hundreds of millions of players. - Built and scaled a 40+ person cross-functional R&D engineering organization across machine learning engineering, data engineering, developer tools, and software development, evolving an initial research group into a full R&D and product-delivery organization. - Led innovation strategy for trust, safety, and player behavior technologies across multiple Activision studios, solving ambiguous problems in fraud, cheating, and player integrity without an existing playbook. - Partnered with senior leaders across Activision, Xbox, and Microsoft to align organizations with competing priorities, creating shared technical direction and ensuring new concepts had support across engine teams, security, analytics, and studio leadership. - Defined the experimentation pipelines, research partnerships, and prototyping frameworks that enabled rapid 0 to 1 concept development, including collaborations with Stanford and NVIDIA. - Absorbed cross-studio strategic and operational complexity so principal engineers and researchers could focus on invention without being pulled into misaligned or high-conflict executive discussions. - Drove the roadmap for emerging technologies used by hundreds of millions of players, contributing to Activision's recognition as a Fast Company Most Innovative Companies in 2024.
- Led early R&D and machine learning initiatives for cheating, fraud, and player behavior, creating the foundations for the cross-functional engineering organization later scaled at the Director level. - Developed novel detection systems and behavioral modeling approaches capable of operating at global scale, supporting live games with tens of millions of players. - Directed research and prototyping work that became key components of Activision’s trust and safety innovation pipeline, including collaborations with Stanford and NVIDIA. - Partnered with engineering, game studios, security, and analytics teams to align on problem definitions, data strategy, and model architecture, reducing ambiguity and accelerating adoption of new methods. - Established technical best practices, model lifecycle standards, and platform patterns that enabled reliable experimentation in high-stakes, real-time environments. - Served as a bridge between senior leadership and engineering teams, translating complex technical challenges into clear narratives that drove priority-setting and executive decision making.
As an expert machine learning engineer, I implement and oversee machine learning projects to detect and minimize disruptive behavior in online gaming. - Built first-generation prototypes for behavioral detection models, combining gameplay signals, telemetry, and threat patterns to evaluate feasibility for production systems. - Designed and implemented real-time ML pipelines using Kafka and Spark Structured Streaming to detect cheating, fraud, and disruptive behavior at global scale. - Developed core engineering standards for packaging, versioning, containerization, CI/CD, and scheduling that enabled reliable deployment of ML workloads across game studios. - Created analysis tooling and model evaluation frameworks that helped teams understand detection performance, operational behavior, and edge cases in live environments. - Translated ambiguous detection problems into concrete model architectures and signal-processing approaches, providing technical clarity for engineering, data science, and security stakeholders.