Senior Software Engineer, Machine Learning

Job not on LinkedIn

🕒 Yesterday

🏄 California – Remote

🏄 California – Remote

💵 $155.6k - $320.3k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Zigsaw

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2016

Making Job-search and talent discovery simpler, faster and effective for Job-seekers.

📋 Description

• Write production Python that powers real-time bidding, model training, and campaign optimization • Train, deploy, and monitor ML models that decide which ads to show, when, and at what price: millions of bid decisions per second • Build and improve our incrementality measurement systems: helping advertisers understand the true causal lift of their CTV spend • Design and implement new ML products across the ad-buying lifecycle: audience targeting, bid optimization, pacing, and attribution • Use LLMs and generative AI to build internal tools that accelerate how we develop, test, and ship ML systems • Serve as a technical lead and mentor on a distributed engineering team

🎯 Requirements

• Strong production Python skills: you write code that runs in prod, not just notebooks • Solid statistics and ML fundamentals: you can reason about experiment design, model evaluation, and when simpler approaches beat complex ones • Familiarity with modern AI tools and good judgment about where they add value • Adtech or CTV experience: familiarity with RTB, programmatic advertising, supply-path optimization • Clear written communication: we're a distributed team and writing is how decisions get made • Comfort with ambiguity: you'll own problems end-to-end in a fast-moving environment, from scoping to shipping • Bachelor's degree in Computer Science, Mathematics, Engineering, related field, or equivalent experience • 4+ years of industry experience • Nice-to-Haves: • Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring • Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration • Causal inference: uplift modeling, synthetic controls, difference-in-differences, or incrementality testing • Big data experience with Scala and Spark • Systems programming experience in Zig or similar (C, C++, Rust) • Reinforcement learning or bandit algorithms in production • Experience building agentic AI systems or LLM-powered workflows • MLOps experience: model deployment, monitoring, and pipeline orchestration on AWS

🏖️ Benefits

• Equity • Health insurance • 401(k) matching • Flexible work hours • Professional development opportunities