Software Engineer Ii, Simulation, Tvscientific

Pinterest Pinterest · Consumer · San Francisco, CA · tvScientific

Software Engineer to build simulation and AI capabilities for a CTV advertising platform. The role involves designing and implementing systems to model the CTV advertising ecosystem, developing AI-driven tools to accelerate ML system development, and using simulation to de-risk ML model deployments. The engineer will also define technical direction and mentor others.

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 into rigorous simulation frameworks
  • Clear written communication
  • Ownership
  • 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

Nice to have

  • Strong production Python skills and experience building simulation or modeling systems
  • Causal inference
  • Experience with discrete event simulation, Monte Carlo methods, or digital twins
  • Reinforcement learning
  • Experience building agentic AI systems or multi-agent simulations
  • Big data experience with Scala and Spark
  • MLOps experience

What the JD emphasized

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

Other signals

  • simulation environments
  • AI agents
  • de-risk ML deployments
  • simulation and AI infrastructure