Sr. Software Engineer, 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

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

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