Launch Potato
Senior Machine Learning Engineer, Recommendation Systems
Launch Potato, Chicago, Illinois, United States, 60290
Overview
Senior Machine Learning Engineer, Recommendation Systems at Launch Potato. You will build, deploy, and scale the personalization engine powering real-time recommendations across millions of user journeys, impacting engagement, retention, and revenue at scale. Launch Potato is a profitable digital media company reaching over 30M+ monthly visitors across brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. As The Discovery and Conversion Company, we 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 value speed, ownership, and measurable impact. Responsibilities
Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale 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
5+ 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–$220,000 per year, paid semi-monthly. Base salary is set according to market rates and varies based on Launch Potato’s Levels Framework. Your compensation package includes a base salary, profit-sharing bonus, and competitive benefits. Role & Culture
Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experience for millions of users daily. You’ll own modeling, feature engineering, data pipelines, and experimentation to make personalization smarter and faster. Diversity, Equity, and Inclusion
Since day one, we’ve been committed to having a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company. We value 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, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Engagement details
Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Advertising Services
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Senior Machine Learning Engineer, Recommendation Systems at Launch Potato. You will build, deploy, and scale the personalization engine powering real-time recommendations across millions of user journeys, impacting engagement, retention, and revenue at scale. Launch Potato is a profitable digital media company reaching over 30M+ monthly visitors across brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. As The Discovery and Conversion Company, we 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 value speed, ownership, and measurable impact. Responsibilities
Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale 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
5+ 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–$220,000 per year, paid semi-monthly. Base salary is set according to market rates and varies based on Launch Potato’s Levels Framework. Your compensation package includes a base salary, profit-sharing bonus, and competitive benefits. Role & Culture
Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experience for millions of users daily. You’ll own modeling, feature engineering, data pipelines, and experimentation to make personalization smarter and faster. Diversity, Equity, and Inclusion
Since day one, we’ve been committed to having a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company. We value 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, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Engagement details
Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Advertising Services
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