Launch Potato is looking to solve the company's most complex ML challenges in personalization and influence strategy, architecture, and innovation across teams.
Requirements
- Expertise building ML systems with deep expertise in large-scale personalization
- 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
Responsibilities
- 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
Other
- Recognized industry expertise through patents, publications, or significant product impact
- Balances deep technical rigor with a strong understanding of product impact.
- Drives decision-making and builds consensus across teams and stakeholders without direct authority.
- Coaches senior engineers, facilitates technical reviews, and raises the technical bar for the entire organization.
- Represents LP in the ML community and contributes to growing its reputation as a leader in ML innovation.
