Launch Potato
Senior ML Engineer, Recommendation Systems
Launch Potato, Houston, Texas, United States, 77246
Overview
Senior ML Engineer, Recommendation Systems at Launch Potato. You will build, deploy, and scale ML systems powering real-time recommendations across millions of user journeys. This role involves designing models, feature engineering, data pipelines, and experimentation to drive engagement, retention, and revenue. Launch Potato is a profitable digital media company that reaches 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. We are headquartered in South Florida with a remote-first team spanning over 15 countries, built around speed, ownership, and measurable impact. Base salary: $130,000$220,000 per year, paid semi-monthly. Were hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. Youll design, deploy, and scale ML systems that power personalization for millions of users and 100M+ predictions daily. We offer a performance-driven environment where future increases are based on company and personal performance. We are an Equal Employment Opportunity employer and value diversity, equity, and inclusion. Must Have
Youve shipped large-scale ML systems into production that power personalization at scale. Youre fluent in ranking algorithms and know how to turn data into engagement and conversions. 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) 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 Your Role
Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experiences for millions of users daily. Youll own modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful. 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 Competencies
Technical Mastery: ML architecture, deployment, and tradeoffs Experimentation Infrastructure: MLflow, Weights & Biases Impact-Driven: Models that move revenue, retention, or engagement Collaborative: Work with engineers, PMs, and analysts Analytical Thinking: Rigorous test methodologies Ownership Mentality: Post-deployment model ownership Execution-Oriented: Production-grade systems with rigor Curious & Innovative: Apply ML advances to personalization Compensation and Benefits
Total compensation includes base salary, profit-sharing bonus, and competitive benefits. The base salary is aligned to market rates and varies by our Levels Framework. Launch Potato is a performance-driven company where increases depend on company and personal performance. Legal and Inclusion
Launch Potato is committed to a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy and related conditions), sexual orientation, gender identity, gender expression, age, protected veteran status, disability, or other legally protected characteristics. #J-18808-Ljbffr
Senior ML Engineer, Recommendation Systems at Launch Potato. You will build, deploy, and scale ML systems powering real-time recommendations across millions of user journeys. This role involves designing models, feature engineering, data pipelines, and experimentation to drive engagement, retention, and revenue. Launch Potato is a profitable digital media company that reaches 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. We are headquartered in South Florida with a remote-first team spanning over 15 countries, built around speed, ownership, and measurable impact. Base salary: $130,000$220,000 per year, paid semi-monthly. Were hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. Youll design, deploy, and scale ML systems that power personalization for millions of users and 100M+ predictions daily. We offer a performance-driven environment where future increases are based on company and personal performance. We are an Equal Employment Opportunity employer and value diversity, equity, and inclusion. Must Have
Youve shipped large-scale ML systems into production that power personalization at scale. Youre fluent in ranking algorithms and know how to turn data into engagement and conversions. 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) 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 Your Role
Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experiences for millions of users daily. Youll own modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful. 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 Competencies
Technical Mastery: ML architecture, deployment, and tradeoffs Experimentation Infrastructure: MLflow, Weights & Biases Impact-Driven: Models that move revenue, retention, or engagement Collaborative: Work with engineers, PMs, and analysts Analytical Thinking: Rigorous test methodologies Ownership Mentality: Post-deployment model ownership Execution-Oriented: Production-grade systems with rigor Curious & Innovative: Apply ML advances to personalization Compensation and Benefits
Total compensation includes base salary, profit-sharing bonus, and competitive benefits. The base salary is aligned to market rates and varies by our Levels Framework. Launch Potato is a performance-driven company where increases depend on company and personal performance. Legal and Inclusion
Launch Potato is committed to a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy and related conditions), sexual orientation, gender identity, gender expression, age, protected veteran status, disability, or other legally protected characteristics. #J-18808-Ljbffr