Principal Machine Learning Engineer - Remote Eligible

Principal Machine Learning Engineer

Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. As The Discovery and Conversion Company, our mission is to connect consumers with the world's leading brands through data-driven content and technology.

Headquartered in South Florida with a remote-first team spanning over 15 countries, we've built a high-growth, high-performance culture where speed, ownership, and measurable impact drive success.

Your Role

As our Principal ML Engineer, you'll be the technical visionary for our personalization and optimization systems. This is an individual contributor role for a deep technical expert who will define our ML architecture, solve our hardest technical challenges, and influence ML strategy across the company.

What You'll Do

• Design company-wide personalization architecture and strategy
• Solve complex technical challenges (cold start, exploration/exploitation, real-time learning)
• Research and implement state-of-the-art ML techniques
• Define standards and patterns used across all ML teams
• Lead cross-functional initiatives spanning multiple quarters
• Mentor senior engineers and review critical technical decisions
• Represent the company in the external ML community

What We're Looking For

• 10+ years building ML systems, with deep personalization expertise
• Recognized expert in ML systems (publications, patents, or industry impact)
• Experience architecting ML platforms serving billions of predictions
• Track record of 0→1 innovation in personalization systems
• Expertise in multiple approaches (deep learning, bandits, causal ML, graph methods)
• Ability to influence without authority and drive consensus
• Exceptional communication skills for technical and executive audiences

Technical Expertise Required:

• Advanced ML architectures at scale
• Real-time ML systems and edge deployment
• Multi-stakeholder marketplace optimization
• Online learning and adaptive systems
• Privacy-preserving personalization