Performance TV Advertising Platform
Senior Software Engineer
Location
California
Posted
35 days ago
Salary
$155.6K - $320.3K / year
Seniority
Senior
Job Description
Senior Software Engineer
tvScientific
• 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
Job Requirements
- 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
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