Launch Potato
Lead Machine Learning Engineer, Recommendation Systems
Launch Potato, Oklahoma City, Oklahoma, United States, 73116
Overview
Lead Machine Learning Engineer, Recommendation Systems at Launch Potato. You will design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys. This role focuses on personalization engines for a portfolio of brands and impacts engagement, retention, and revenue at scale. Responsibilities
Drive business growth by building and optimizing the recommendation systems that personalize experiences for millions of users daily. Own modeling, feature engineering, data pipelines, and experimentation to improve personalization. Design, deploy, and scale ML systems that power real-time recommendations. Build and deploy ML models serving 100M+ predictions per day. Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements. 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 efficiency 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
Must Have 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) Skilled with 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. Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. The compensation package includes a base salary, profit-sharing bonus, and competitive benefits. About the Company and Culture
Launch Potato is a profitable digital media company reaching over 30M+ monthly visitors with brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. We operate as The Discovery and Conversion Company, connecting consumers with leading brands through data-driven content and technology. We are headquartered in South Florida with a remote-first team spanning over 15 countries and a culture focused on speed, ownership, and measurable impact. We are an Equal Employment Opportunity company and value diversity, equity, and inclusion. Want to accelerate your career? Apply now! Legal Considerations
We are committed to an inclusive workplace and do not discriminate based on race, religion, color, national origin, gender (including pregnancy and related conditions), sexual orientation, gender identity, gender expression, age, veteran status, disability status, or other legally protected characteristics.
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Lead Machine Learning Engineer, Recommendation Systems at Launch Potato. You will design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys. This role focuses on personalization engines for a portfolio of brands and impacts engagement, retention, and revenue at scale. Responsibilities
Drive business growth by building and optimizing the recommendation systems that personalize experiences for millions of users daily. Own modeling, feature engineering, data pipelines, and experimentation to improve personalization. Design, deploy, and scale ML systems that power real-time recommendations. Build and deploy ML models serving 100M+ predictions per day. Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements. 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 efficiency 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
Must Have 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) Skilled with 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. Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. The compensation package includes a base salary, profit-sharing bonus, and competitive benefits. About the Company and Culture
Launch Potato is a profitable digital media company reaching over 30M+ monthly visitors with brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. We operate as The Discovery and Conversion Company, connecting consumers with leading brands through data-driven content and technology. We are headquartered in South Florida with a remote-first team spanning over 15 countries and a culture focused on speed, ownership, and measurable impact. We are an Equal Employment Opportunity company and value diversity, equity, and inclusion. Want to accelerate your career? Apply now! Legal Considerations
We are committed to an inclusive workplace and do not discriminate based on race, religion, color, national origin, gender (including pregnancy and related conditions), sexual orientation, gender identity, gender expression, age, veteran status, disability status, or other legally protected characteristics.
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