Senior Software Engineer, Machine Learning

This is a fully remote position, open to applicants in California.

📋 Description

• Develop production-level Python code that supports real-time bidding, model training, and campaign optimization.

• Train, deploy, and oversee ML models that determine which ads to display, when to show them, and at what price, processing millions of bid decisions each second.

• Create and enhance our incrementality measurement systems to assist advertisers in understanding the genuine causal impact of their CTV expenditures.

• Design and execute new ML products throughout the ad-buying lifecycle, including audience targeting, bid optimization, pacing, and attribution.

• Leverage LLMs and generative AI to develop internal tools that streamline the development, testing, and deployment of ML systems.

• Act as a technical lead and mentor within a distributed engineering team.

⛳️ Requirements

• Proficient in production-level Python: you write code that operates in production environments, not just in notebooks.

• Strong understanding of statistics and ML fundamentals: you possess the ability to reason about experiment design, model evaluation, and when simpler methods outperform complex ones.

• Familiarity with contemporary AI tools and the discernment to identify where they provide value.

• Experience in adtech or CTV: understanding of RTB, programmatic advertising, and supply-path optimization.

• Excellent written communication skills: as a distributed team, effective writing is essential for decision-making.

• Ability to navigate ambiguity: you will take ownership of problems from start to finish in a dynamic environment, from scoping to delivery.

• Bachelor's degree in Computer Science, Mathematics, Engineering, a related field, or equivalent experience.

• Over 4 years of industry experience.

• Nice-to-Haves:

• Experience with tools like 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 enhancing engineering workflows.

• Knowledge of causal inference methods such as uplift modeling, synthetic controls, difference-in-differences, or incrementality testing.

• Big data experience with Scala and Spark.

• Systems programming experience in Zig or similar languages (C, C++, Rust).

• Experience with reinforcement learning or bandit algorithms in production.

• Experience in building agentic AI systems or LLM-powered workflows.

• MLOps experience, including model deployment, monitoring, and pipeline orchestration on AWS.

🏝️ Benefits

• Equity

• Health insurance

• 401(k) matching

• Flexible work hours

• Professional development opportunities