Sr. Software Engineer, Simulation, Tvscientific

Pinterest Pinterest · Consumer · San Francisco, CA · tvScientific

Senior Software Engineer role focused on building simulation environments and AI-driven tools for the CTV advertising ecosystem. The role involves designing systems to model auction dynamics, bidding strategies, and campaign outcomes, and developing AI agents to automate experimentation and de-risk ML model deployments. Experience with modern AI tools, probabilistic modeling, and adtech is required.

What you'd actually do

  1. Design and build simulation environments that model CTV auction mechanics, inventory supply, and advertiser competition
  2. Develop counterfactual and what-if frameworks for evaluating bidding strategies, budget allocation, and pacing algorithms offline
  3. Build AI agents that explore strategy spaces, generate hypotheses, and automate experimentation within simulated environments
  4. Use simulation to de-risk ML model deployments — validate new bidding and optimization strategies before they touch live traffic
  5. Define the technical direction for simulation and AI infrastructure and mentor engineers on the team

Skills

Required

  • 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
  • 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
  • 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
  • High integrity and ownership

Nice to have

  • 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

What the JD emphasized

  • Hands-on experience with modern AI tools: LLMs, code generation, agentic workflows
  • 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

Other signals

  • Build AI agents that explore strategy spaces, generate hypotheses, and automate experimentation within simulated environments
  • Use simulation to de-risk ML model deployments
  • Hands-on experience with modern AI tools: LLMs, code generation, agentic workflows
  • 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