Sr. Software Engineer, Simulation, tvScientific

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

• 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.

• 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

US based applicants only $155,584—$320,320 USD

Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws.

We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support.

Information regarding the culture at Pinterest and benefits available for this position can be found here.