Key skills
Production PythonMachine Learning fundamentalsAdtechCTV experienceBig data experienceMLOps experienceComfort with ambiguityTeaching experienceCausal inferenceSystems programmingReinforcement learningLLM-powered workflowsClear written communicationPythonC++CRustScalaAIMachine LearningMLGenerative AILLMMLOpsSparkAWSCommunication
About this role
Pinterest is a platform that inspires creativity and helps users plan memorable experiences. As a Sr. Machine Learning Engineer at tvScientific, you will build ML and AI systems for their Connected TV ad-buying platform, 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
- Teaching experience
- 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