Note: The job is a remote job and is open to candidates in USA. Pinterest is a platform that helps people discover creative ideas and plan for the future. The Senior Machine Learning Engineer will build ML and AI systems for tvScientific’s Connected TV advertising platform, including real-time bidding, campaign optimization, and incrementality measurement. The role also involves deploying and monitoring models, developing ML products, using generative AI tools, and mentoring a distributed engineering team.

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

Skills

• 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
• US based applicants only
• 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

Benefits

• The position is also eligible for equity.
• Flexibility to do your best work
• This position is not eligible for relocation assistance.

Company Overview

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