Principal Machine Learning Engineer, Recommendation Systems

MUST HAVE:

• 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

EXPERIENCE: 10+ years designing, developing, and deploying large-scale machine learning systems with a focus on personalization

YOUR ROLE
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.

OUTCOMES

• 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

COMPETENCIES

• Visionary Thinking: Defines the future state of ML infrastructure and personalization strategy. Balances deep technical rigor with a strong understanding of product impact.
• Architectural Excellence: Designs scalable, modular systems that support billions of predictions and enable rapid experimentation. Sets high standards for maintainability and performance.
• Technical Influence: Drives decision-making and builds consensus across teams and stakeholders without direct authority. Provides trusted guidance in ambiguous situations.
• Expert-Level Execution: Navigates deep technical complexity while maintaining execution velocity. Translates research insights into production-ready solutions.
• Mentorship & Leadership: Coaches senior engineers, facilitates technical reviews, and raises the technical bar for the entire organization.
• External Thought Leadership: Represents LP in the ML community and contributes to growing its reputation as a leader in ML innovation.
• Curious & Forward-Looking: Constantly explores new approaches to personalization and actively prototypes cutting-edge methodologies.