Zigsaw

Machine Learning Engineer

Zigsaw

full-time

Posted on:

Location Type: Remote

Location: CaliforniaUnited States

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Salary

💰 $123,696 - $254,667 per year

About the role

  • 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 LLMs and generative AI to accelerate internal ML workflows — synthetic data generation, code generation, automated analysis, and rapid prototyping
  • 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

  • Strong production Python skills and experience building simulation or modeling systems
  • 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 auction theory, 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
  • Nice-to-Haves:
  • 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
  • Systems programming experience in Zig or similar (C, C++, Rust)
  • MLOps experience — model deployment, monitoring, and pipeline orchestration on AWS
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
Pythonprobabilistic modelingstochastic processesagent-based simulationcausal inferencediscrete event simulationMonte Carlo methodsreinforcement learningMLOpsbig data
Soft Skills
clear written communicationownershipmentoringjudgment