Key responsibilities include:

• 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

Requirements include:

• 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 include:

• 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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What you'll do

• Design and build scalable backend infrastructure for content moderation, abuse detection and agents guardrails, deploying AI/ML models into production systems
• Architect robust APIs, data pipelines, and service architectures supporting real-time and batch moderation workflows

What you need

• 6+ years of backend software engineering experience building production systems at scale
• Strong production backend experience: distributed systems, APIs, data pipelines, and Python expertise (asynchronous Python, backend frameworks)
• Infrastructure & DevOps proficiency: cloud platforms (AWS/GCP), containerization (Docker/K8s), CI/CD pipelines

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