Senior Software Engineer

Emploi pas sur LinkedIn

🕒 il y a 1 mois

🏄 California – Distant

🏄 California – Remote

💵 $155 584 - $320 320 / an

⏰ Temps Plein

🟠 Senior

🧑‍💻 Développeur Full-Stack

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Zigsaw

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11 - 50 employés

Fondée en 2016

Simplifier, accélérer et rendre plus efficace la recherche d'emploi et la découverte de talents pour les chercheurs d'emploi.

Description

• 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

🎯 Exigences

• 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