Launch Potato
Senior ML Engineer, Recommendation Systems
Launch Potato, Greenville, South Carolina, us, 29610
Overview
Senior ML Engineer, Recommendation Systems at Launch Potato. You will design, deploy, and scale machine learning systems that power real-time recommendations across millions of user journeys, with the goal of improving engagement, retention, and revenue. Base salary: $130,000$220,000 per year, paid semi-monthly. Must Have
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 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 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 Improve data processing pipelines (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 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: Interpret data trends and design rigorous tests Ownership Mentality: Post-deployment model ownership and improvement Execution-Oriented: Production-grade systems with rigor Curious & Innovative: Stay updated on ML advances for personalization Benefits and Culture
Launch Potato is a profitable, remote-first company with a diverse team. We are an Equal Employment Opportunity employer. We value diversity, equity, and inclusion. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, gender identity, age, veteran status, disability, or other legally protected characteristics. #J-18808-Ljbffr
Senior ML Engineer, Recommendation Systems at Launch Potato. You will design, deploy, and scale machine learning systems that power real-time recommendations across millions of user journeys, with the goal of improving engagement, retention, and revenue. Base salary: $130,000$220,000 per year, paid semi-monthly. Must Have
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 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 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 Improve data processing pipelines (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 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: Interpret data trends and design rigorous tests Ownership Mentality: Post-deployment model ownership and improvement Execution-Oriented: Production-grade systems with rigor Curious & Innovative: Stay updated on ML advances for personalization Benefits and Culture
Launch Potato is a profitable, remote-first company with a diverse team. We are an Equal Employment Opportunity employer. We value diversity, equity, and inclusion. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, gender identity, age, veteran status, disability, or other legally protected characteristics. #J-18808-Ljbffr