Sr. Software Engineer, Simulation, tvScientific

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .

About tvScientific

tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business.

We are seeking a Sr. Software Engineer to build out our simulation and AI capabilities. You'll design and implement systems that model the CTV advertising ecosystem — auction dynamics, bidding strategies, campaign outcomes, and counterfactual scenarios — and develop AI-driven tools that accelerate how we build, test, and deploy ML systems.

What you’ll do:

• Design and build simulation environments that model CTV auction mechanics, inventory supply, and advertiser competition


• Develop counterfactual and what-if frameworks for evaluating bidding strategies, budget allocation, and pacing algorithms offline


• Build AI agents that explore strategy spaces, generate hypotheses, and automate experimentation within simulated environments


• Use simulation to de-risk ML model deployments — validate new bidding and optimization strategies before they touch live traffic


• Define the technical direction for simulation and AI infrastructure and mentor engineers on the team

What we’re looking for:

• Systems programming experience in Zig or similar (C, C++, Rust)


• Deep understanding of probabilistic modeling, stochastic processes, or agent-based simulation


• Hands-on experience with modern AI tools: LLMs, code generation, agentic workflows — and good judgment about when they help vs. when they don't


• Adtech experience: you understand RTB mechanics, and the dynamics of programmatic advertising


• Ability to translate business questions ("what happens if we change our bid strategy?") into rigorous simulation frameworks


• Clear written communication: you'll be defining new technical directions and need to bring others along


• Ownership: you scope, design, and ship systems end-to-end with minimal direction


• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs


• Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)


• High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables.


• ​Degree in a relevant field such as computer science, statistics, engineering, or equivalent experience.
  

   



• Nice-to-Haves:
  
  
  
  • Strong production Python skills and experience building simulation or modeling systems
  
  
  • Causal inference — uplift modeling, synthetic controls, difference-in-differences, or incrementality testing
  
  
  • Experience with discrete event simulation, Monte Carlo methods, or digital twins
  
  
  • Reinforcement learning — using simulated environments for policy learning and evaluation
  
  
  • Experience building agentic AI systems or multi-agent simulations
  
  
  • Big data experience with Scala and Spark
  
  
  • MLOps experience — model deployment, monitoring, and pipeline orchestration on AWS
  
  

In-Office Requirement Statement:

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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