Sr. Machine Learning Engineer, tvScientific

<div class="content-intro"><p><strong>About Pinterest:</strong></p> <p>Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.</p> <p>Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the <a href="https://www.pinterestcareers.com/our-life/pinflex/">flexibility</a> to do your best work. Creating a career you love? It’s Possible.</p></div><p><strong>About tvScientific</strong></p> <p>tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business.</p> <p> </p> <p>As a Sr. Machine Learning Engineer at tvScientific, you'll build the ML and AI systems behind our Connected TV ad-buying platform: real-time bidding, campaign optimization, and incrementality measurement at scale. We're an adtech company solving a hard problem: making CTV advertising actually measurable. Our platform helps advertisers buy ads across the CTV ecosystem: Hulu, Pluto TV, Disney+, HBO Max, and hundreds of FAST channels: and prove that those ads drove real business outcomes.</p> <p><strong><br>What you'll do:</strong></p> <ul> <li>Write production Python that powers real-time bidding, model training, and campaign optimization</li> <li>Train, deploy, and monitor ML models that decide which ads to show, when, and at what price: millions of bid decisions per second</li> <li>Build and improve our incrementality measurement systems: helping advertisers understand the true causal lift of their CTV spend</li> <li>Design and implement new ML products across the ad-buying lifecycle: audience targeting, bid optimization, pacing, and attribution</li> <li>Use LLMs and generative AI to build internal tools that accelerate how we develop, test, and ship ML systems</li> <li>Serve as a technical lead and mentor on a distributed engineering team</li> </ul> <p><strong><br>What we're looking for:</strong></p> <ul> <li>Strong production Python skills: you write code that runs in prod, not just notebooks</li> <li>Solid statistics and ML fundamentals: you can reason about experiment design, model evaluation, and when simpler approaches beat complex ones</li> <li>Familiarity with modern AI tools and good judgment about where they add value</li> <li>Adtech or CTV experience: familiarity with RTB, programmatic advertising, supply-path optimization</li> <li>Clear written communication: we're a distributed team and writing is how decisions get made</li> <li>Comfort with ambiguity: you'll own problems end-to-end in a fast-moving environment, from scoping to shipping</li> <li><strong>Nice-to-Haves:</strong> <ul> <li>Teaching experience</li> <li>Causal inference: uplift modeling, synthetic controls, difference-in-differences, or incrementality testing</li> <li>Big data experience with Scala and Spark</li> <li>Systems programming experience in Zig or similar (C, C++, Rust)</li> <li>Reinforcement learning or bandit algorithms in production</li> <li>Experience building agentic AI systems or LLM-powered workflows</li> <li>MLOps experience: model deployment, monitoring, and pipeline orchestration on AWS</li> </ul> </li> </ul> <p> </p> <p><strong>In-Office Requirement Statement:</strong></p> <ul> <li>We recognize that the ideal environment for work is situational ... (truncated, view full listing at source)