Senior Software Engineer

Vaga não está no LinkedIn

🕒 Abril 23

🏄 California – Remoto

🏄 California – Remote

💵 $155.584 - $320.320 / ano

⏰ Tempo Integral

🟠 Sênior

🧑‍💻 Engenheiro Full-stack

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tvScientific

SiteLinkedInTodas as Vagas

51 - 200 funcionários

Fundada em 2020

📱 Mídia

☁️ SaaS

🤝 B2B

Media • SaaS • B2B

tvScientific é uma plataforma de publicidade em TV Conectada (CTV) focada em performance que utiliza otimização baseada em IA para fornecer resultados de negócios mensuráveis, como instalações, vendas e tráfego. A plataforma oferece acesso a inventário, segmentação, gerenciamento criativo, mensuração e atribuição, além de relatórios em tempo real com um painel unificado e análises transparentes. A tvScientific se posiciona como uma solução paga por performance para marcas de consumo, oferecendo resultados garantidos, controles de segurança de marca e integrações para escala.

Descrição

• 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

🎯 Requisitos

• 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