Launch Potato
Lead Machine Learning Engineer, Recommendation Systems
Launch Potato, Denver, Colorado, United States, 80285
Overview
Lead Machine Learning Engineer, Recommendation Systems. Launch Potato is a profitable digital media company that reaches millions of 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. This role gives you the chance to work on systems serving 100M+ predictions daily, directly impacting engagement, retention, and revenue at scale. BASE SALARY:
$130,000–$250,000 per year, paid semi-monthly Location:
Remote-friendly with a presence in Denver, CO for some opportunities (note: refer to job post for location specifics).
Responsibilities / Must Have
Shipped large-scale ML systems into production that power personalization at scale 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
Your Role
Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experiences for millions of users daily. You’ll own modeling, feature engineering, data pipelines, and experimentation to make personalization smarter, faster, and more impactful.
Outcomes
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 under 50ms across key product surfaces 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 (W&B) Impact-Driven: Models that move revenue, retention, or engagement Collaborative: Works with engineers, PMs, and analysts to scope features Analytical Thinking: Analyzes data trends and designs rigorous tests Ownership Mentality: Owns models post-deployment and iterates Execution-Oriented: Delivers production-grade systems quickly with rigor Curious & Innovative: Stays updated on ML advances and applies them to personalization
Compensation
Total compensation includes base salary, profit-sharing bonus, and benefits. The base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. We are a performance-driven company and future increases are based on company and individual performance.
Equity and Inclusion
Launch Potato is committed to an inclusive, diverse team and culture. We are an Equal Employment Opportunity employer. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy or related conditions), sexual orientation, gender identity or expression, age, veteran status, disability status, or other legally protected characteristics.
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Lead Machine Learning Engineer, Recommendation Systems. Launch Potato is a profitable digital media company that reaches millions of 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. This role gives you the chance to work on systems serving 100M+ predictions daily, directly impacting engagement, retention, and revenue at scale. BASE SALARY:
$130,000–$250,000 per year, paid semi-monthly Location:
Remote-friendly with a presence in Denver, CO for some opportunities (note: refer to job post for location specifics).
Responsibilities / Must Have
Shipped large-scale ML systems into production that power personalization at scale 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
Your Role
Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experiences for millions of users daily. You’ll own modeling, feature engineering, data pipelines, and experimentation to make personalization smarter, faster, and more impactful.
Outcomes
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 under 50ms across key product surfaces 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 (W&B) Impact-Driven: Models that move revenue, retention, or engagement Collaborative: Works with engineers, PMs, and analysts to scope features Analytical Thinking: Analyzes data trends and designs rigorous tests Ownership Mentality: Owns models post-deployment and iterates Execution-Oriented: Delivers production-grade systems quickly with rigor Curious & Innovative: Stays updated on ML advances and applies them to personalization
Compensation
Total compensation includes base salary, profit-sharing bonus, and benefits. The base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. We are a performance-driven company and future increases are based on company and individual performance.
Equity and Inclusion
Launch Potato is committed to an inclusive, diverse team and culture. We are an Equal Employment Opportunity employer. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy or related conditions), sexual orientation, gender identity or expression, age, veteran status, disability status, or other legally protected characteristics.
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