tvScientific

Connected TV Advertising + Attribution Platform

Senior Machine Learning Engineer

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteTeam 51-200Since 2020H1B No SponsorCompany SiteLinkedIn

Location

California

Posted

12 days ago

Salary

$155.6K - $320.3K / year

Bachelor DegreeEnglishAWSPythonRustScalaSpark

Job Description

• 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

Job 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
  • Nice-to-Haves:
  • 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

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