Launch Potato
Lead Machine Learning Engineer, Recommendation Systems
Launch Potato, West Columbia, South Carolina, United States, 29172
Overview
Lead Machine Learning Engineer, Recommendation Systems at Launch Potato. Join a profitable digital media company delivering data-driven content and technology to connect consumers with leading brands. Headquartered in South Florida with a remote-first team spanning 15+ countries. We are hiring to build the personalization engine behind our portfolio of brands. Youll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys and deliver 100M+ predictions daily. Responsibilities
Own modeling, feature engineering, data pipelines, and experimentation to improve personalization at scale Build and deploy ML models serving 100M+ predictions per day Enhance data processing pipelines (Spark, Beam, Dask) for efficiency and reliability Design ranking algorithms balancing relevance, diversity, and revenue Deliver real-time personalization with latency
Run statistically rigorous A/B tests to measure true business impact Optimize latency, throughput, and production cost Collaborate with product, engineering, and analytics teams to launch high-impact features Implement monitoring and maintain clear ownership for model reliability Must Have / Qualifications
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) 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 Role Outcomes
Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Improve data processing pipelines for efficiency and reliability Design ranking algorithms balancing relevance, diversity, and revenue Deliver real-time personalization with latency
Run rigorous A/B tests to measure business impact Optimize latency, throughput, and cost in production Partner with product, engineering, and analytics to launch features Implement monitoring systems and own 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 Analytical Thinking: Analyzes data trends and designs robust tests Ownership: Post-deployment model ownership and ongoing improvement Execution-Oriented: Production-grade systems with rigor Curious & Innovative: Applies ML advances to personalization Total Compensation
Base salary is set according to market rates; compensation includes base salary, profit-sharing bonus, and benefits. Future increases are based on performance. Why Join Us
Were a diverse, inclusive team committed to equal employment opportunity. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, gender identity, age, veteran status, disability, or other protected characteristics. Want to accelerate your career? Apply now! #J-18808-Ljbffr
Lead Machine Learning Engineer, Recommendation Systems at Launch Potato. Join a profitable digital media company delivering data-driven content and technology to connect consumers with leading brands. Headquartered in South Florida with a remote-first team spanning 15+ countries. We are hiring to build the personalization engine behind our portfolio of brands. Youll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys and deliver 100M+ predictions daily. Responsibilities
Own modeling, feature engineering, data pipelines, and experimentation to improve personalization at scale Build and deploy ML models serving 100M+ predictions per day Enhance data processing pipelines (Spark, Beam, Dask) for efficiency and reliability Design ranking algorithms balancing relevance, diversity, and revenue Deliver real-time personalization with latency
Run statistically rigorous A/B tests to measure true business impact Optimize latency, throughput, and production cost Collaborate with product, engineering, and analytics teams to launch high-impact features Implement monitoring and maintain clear ownership for model reliability Must Have / Qualifications
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) 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 Role Outcomes
Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Improve data processing pipelines for efficiency and reliability Design ranking algorithms balancing relevance, diversity, and revenue Deliver real-time personalization with latency
Run rigorous A/B tests to measure business impact Optimize latency, throughput, and cost in production Partner with product, engineering, and analytics to launch features Implement monitoring systems and own 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 Analytical Thinking: Analyzes data trends and designs robust tests Ownership: Post-deployment model ownership and ongoing improvement Execution-Oriented: Production-grade systems with rigor Curious & Innovative: Applies ML advances to personalization Total Compensation
Base salary is set according to market rates; compensation includes base salary, profit-sharing bonus, and benefits. Future increases are based on performance. Why Join Us
Were a diverse, inclusive team committed to equal employment opportunity. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, gender identity, age, veteran status, disability, or other protected characteristics. Want to accelerate your career? Apply now! #J-18808-Ljbffr