Principal ML Engineer, Ad Performance

September 4

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Launch Potato

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Media • B2C

Launch Potato is South Florida's fastest growing digital media company, founded by a group of young and ambitious individuals with a history of building successful direct-to-consumer digital businesses. Headquartered in Delray Beach, FL, Launch Potato operates with a distributed global team. The company focuses on solving complex problems with smart marketing, great engineering, data science, and fun, emphasizing its commitment to innovation and scalability.

51 - 200 employees

Founded 2015

📱 Media

👥 B2C

📋 Description

• Establish the technical vision for personalization at Launch Potato, solving the company’s most complex ML challenges and influencing strategy, architecture, and innovation across teams • Define company-wide personalization strategy and architecture, driving alignment across all ML teams • Solve critical technical challenges such as cold start, real-time learning, and exploration/exploitation tradeoffs • Design and implement advanced ML solutions using cutting-edge techniques (e.g., graph neural networks, causal models, bandit algorithms) • Create and enforce ML architecture patterns, design standards, and reusable infrastructure across teams • Lead multi-quarter, cross-functional initiatives that redefine how personalization impacts business KPIs • Act as technical mentor to senior ML engineers, guiding complex decision-making and scaling team capability • Represent Launch Potato’s technical brand externally through speaking engagements, open source contributions, or publications • Champion privacy-preserving personalization, responsible AI practices, and adaptive learning systems

🎯 Requirements

• Expertise building ML systems with deep expertise in large-scale personalization • Recognized industry expertise through patents, publications, or significant product impact • Proven success architecting ML platforms serving billions of predictions in production • Demonstrated track record of 0→1 innovation in personalization or recommender systems • Mastery across multiple ML domains including deep learning, causal inference, multi-armed bandits, and graph-based models • 10+ years designing, developing, and deploying large-scale machine learning systems with a focus on personalization

🏖️ Benefits

• Profit-sharing bonus • Competitive benefits (unspecified) • Remote-first work arrangement (remote team spanning 15+ countries) • Paid semi-monthly salary • Performance-driven compensation increases based on company and personal performance