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Responsibilities:
- Build simulation environments to model CTV auction mechanics and advertiser competition for offline strategy evaluation.
- Develop AI-driven tools and frameworks for automating experimentation and de-risking ML model deployments before live traffic.
- Define technical direction and mentor engineers on the team, taking ownership of system design and implementation.
Qualifications:
- Systems programming experience in Zig or similar languages, with deep knowledge of probabilistic modeling or agent-based simulation.
- Hands-on experience with modern AI tools like LLMs and adtech, particularly RTB mechanics and programmatic advertising dynamics.
- Strong ability to translate business questions into simulation frameworks, with clear written communication and high integrity for sensitive data.
Preferred Skills:
- Production Python skills and experience with simulation systems, causal inference, or reinforcement learning for policy evaluation.
- Big data experience with Scala and Spark, along with MLOps expertise on AWS for model deployment and pipeline orchestration.
tvScientific
tvScientific is the first and only CTV advertising platform purpose-built for performance marketers, leveraging massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. It is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification, and the company operates with a culture focused on transparency and performance, as part of the Pinterest ecosystem, offering a PinFlex working model.