• Define the companys ML strategy: where ML should be applied across products, what infrastructure is required, and how to approach build vs. buy decisions. • Design and build production ML systems end-to-end — including data pipelines, model training workflows, evaluation frameworks, and inference serving. • Establish rigorous evaluation methodology to measure model quality, detect regressions, and support data-driven iteration. • Own the data strategy: determine what data is needed, how it should be labeled, how feedback loops are structured, and how models continuously improve. • Partner closely with product and backend engineers to integrate ML into customer-facing systems. • Write production-quality code within the existing codebase and contribute to architectural decisions. • Over time, help recruit, mentor, and lead the ML team as the function expands.

• Design and build simulation environments that model CTV auction mechanics, inventory supply, and advertiser competition • Develop counterfactual and what-if frameworks for evaluating bidding strategies, budget allocation, and pacing algorithms • Build AI agents that explore strategy spaces, generate hypotheses, and automate experimentation within simulated environments • Use LLMs and generative AI to accelerate internal ML workflows • Validate new bidding and optimization strategies before they touch live traffic • Define the technical direction for simulation and AI infrastructure and mentor engineers on the team

• Write production Python that powers real-time bidding, model training, and campaign optimization • Train, deploy, and monitor ML models that decide which ads to show, when, and at what price: millions of bid decisions per second • Build and improve our incrementality measurement systems: helping advertisers understand the true causal lift of their CTV spend • Design and implement new ML products across the ad-buying lifecycle: audience targeting, bid optimization, pacing, and attribution • Use LLMs and generative AI to build internal tools that accelerate how we develop, test, and ship ML systems • Serve as a technical lead and mentor on a distributed engineering team

• Scale the decisionmaking process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments • Improve the developer experience for the data science team • Upgrade our observability tooling • Serve as a technical lead and mentor to the team • Make every deployment smooth as our infrastructure evolves.