Sr. Software Engineer, Machine Learning, tvScientific

Pinterest is a platform that inspires creativity and innovation, and they are seeking a Senior Machine Learning Engineer to join their tvScientific team. The role involves building ML and AI systems for Connected TV ad-buying, focusing on real-time bidding and campaign optimization.

Responsibilities:

• 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

Requirements:

• Strong production Python skills: you write code that runs in prod, not just notebooks
• Solid statistics and ML fundamentals: you can reason about experiment design, model evaluation, and when simpler approaches beat complex ones
• Familiarity with modern AI tools and good judgment about where they add value
• Adtech or CTV experience: familiarity with RTB, programmatic advertising, supply-path optimization
• Clear written communication: we're a distributed team and writing is how decisions get made
• Comfort with ambiguity: you'll own problems end-to-end in a fast-moving environment, from scoping to shipping
• Bachelor's degree in Computer Science, Mathematics, Engineering, related field, or equivalent experience
• 4+ years of industry experience
• Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring
• Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration
• Causal inference: uplift modeling, synthetic controls, difference-in-differences, or incrementality testing
• Big data experience with Scala and Spark
• Systems programming experience in Zig or similar (C, C++, Rust)
• Reinforcement learning or bandit algorithms in production
• Experience building agentic AI systems or LLM-powered workflows
• MLOps experience: model deployment, monitoring, and pipeline orchestration on AWS