About the job
We are seeking a 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.
Responsibilities
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
Qualifications
Minimum
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.
Preferred
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