
Machine Learning Engineer
Zigsaw
full-time
Posted on:
Location Type: Remote
Location: California • United 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