Launch Potato
Lead Machine Learning Engineer, Recommendation Systems
Launch Potato, Phoenix, Arizona, United States, 85003
Overview
Lead Machine Learning Engineer, Recommendation Systems
— Lead efforts to build personalization engines powering real-time recommendations across millions of user journeys. Design, deploy, and scale ML systems that deliver 100M+ predictions daily, impacting engagement, retention, and revenue at scale. Responsibilities
Drive business growth by building and optimizing recommendation systems for personalization at scale Own modeling, feature engineering, data pipelines, experimentation, and deployment Build and deploy ML models serving 100M+ predictions per day Enhance data processing pipelines (e.g., Spark, Beam, Dask) for efficiency and reliability Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency
Run statistically rigorous A/B tests to measure true business impact Optimize for latency, throughput, and cost in production Partner with product, engineering, and analytics to launch high-impact personalization features Implement monitoring systems and maintain clear ownership for model reliability Qualifications
7+ years building and scaling production ML systems with measurable business impact Experience deploying ML systems serving 100M+ predictions daily Strong background in ranking algorithms (collaborative filtering, learning-to-rank, deep learning) Proficiency with Python and ML frameworks (TensorFlow or PyTorch) SQL and modern data warehouses (Snowflake, BigQuery, Redshift) plus data lakes Familiarity with distributed computing (Spark, Ray) and LLM/AI Agent frameworks Track record of improving business KPIs via ML-powered personalization Experience with A/B testing platforms and experiment logging best practices Compensation
BASE SALARY:
$130,000–$250,000 per year, paid semi-monthly About Launch Potato
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. We’re hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. You’ll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys. Equal Opportunity
Launch Potato is an Equal Employment Opportunity employer. We are committed to diversity, equity, and inclusion. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, protected veteran status, disability status, or other legally protected characteristics.
#J-18808-Ljbffr
Lead Machine Learning Engineer, Recommendation Systems
— Lead efforts to build personalization engines powering real-time recommendations across millions of user journeys. Design, deploy, and scale ML systems that deliver 100M+ predictions daily, impacting engagement, retention, and revenue at scale. Responsibilities
Drive business growth by building and optimizing recommendation systems for personalization at scale Own modeling, feature engineering, data pipelines, experimentation, and deployment Build and deploy ML models serving 100M+ predictions per day Enhance data processing pipelines (e.g., Spark, Beam, Dask) for efficiency and reliability Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency
Run statistically rigorous A/B tests to measure true business impact Optimize for latency, throughput, and cost in production Partner with product, engineering, and analytics to launch high-impact personalization features Implement monitoring systems and maintain clear ownership for model reliability Qualifications
7+ years building and scaling production ML systems with measurable business impact Experience deploying ML systems serving 100M+ predictions daily Strong background in ranking algorithms (collaborative filtering, learning-to-rank, deep learning) Proficiency with Python and ML frameworks (TensorFlow or PyTorch) SQL and modern data warehouses (Snowflake, BigQuery, Redshift) plus data lakes Familiarity with distributed computing (Spark, Ray) and LLM/AI Agent frameworks Track record of improving business KPIs via ML-powered personalization Experience with A/B testing platforms and experiment logging best practices Compensation
BASE SALARY:
$130,000–$250,000 per year, paid semi-monthly About Launch Potato
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. We’re hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. You’ll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys. Equal Opportunity
Launch Potato is an Equal Employment Opportunity employer. We are committed to diversity, equity, and inclusion. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, protected veteran status, disability status, or other legally protected characteristics.
#J-18808-Ljbffr