Launch Potato
Lead Machine Learning Engineer, Recommendation Systems
Launch Potato, Newark, New Jersey, us, 07175
Overview
Lead Machine Learning Engineer, Recommendation Systems at
Launch Potato . Launch Potato is a profitable digital media company connecting consumers with 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. 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 real-time recommendations across millions of user journeys, serving 100M+ predictions daily to influence engagement, retention, and revenue. Responsibilities
Drive business growth by building and optimizing recommendation systems that personalize experiences for millions of users daily. Own modeling, feature engineering, data pipelines, and experimentation to improve personalization, performance, and impact. Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale. 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 for latency, throughput, and cost in production. Collaborate with product, engineering, and analytics to launch high-impact personalization features. Implement monitoring systems and maintain clear ownership for model reliability. Required 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). 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 KPIs via ML-powered personalization. Experience with A/B testing platforms and experiment logging best practices. 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: Effective with engineers, PMs, and analysts. Analytical Thinking: Strong data-driven decision making and test methodologies. Ownership Mentality: Post-deployment ownership and continuous improvement. Execution-Oriented: Production-grade delivery with rigor. Curious & Innovative: Keeping up with ML advances for personalization. Compensation & Benefits
Base salary: $130,000$250,000 per year, paid semi-monthly. Total compensation includes base salary, profit-sharing bonus, and competitive benefits. Compensation varies by market rates and internal framework. Future increases are based on company and personal performance. About You
Passionate about building scalable ML systems for personalization and delivering measurable business impact. Equal Opportunity
Launch Potato is an Equal Employment Opportunity employer. We value diversity, equity, and inclusion. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy or related medical conditions), sexual orientation, gender identity or expression, age, veteran status, disability, or other protected characteristics. Location & Status
Remote-first with opportunities in Newark, NJ and other locations. Seniority level: Mid-Senior level. Employment type: Full-time. Job function: Engineering and Information Technology. Industries: Advertising Services. #J-18808-Ljbffr
Lead Machine Learning Engineer, Recommendation Systems at
Launch Potato . Launch Potato is a profitable digital media company connecting consumers with 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. 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 real-time recommendations across millions of user journeys, serving 100M+ predictions daily to influence engagement, retention, and revenue. Responsibilities
Drive business growth by building and optimizing recommendation systems that personalize experiences for millions of users daily. Own modeling, feature engineering, data pipelines, and experimentation to improve personalization, performance, and impact. Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale. 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 for latency, throughput, and cost in production. Collaborate with product, engineering, and analytics to launch high-impact personalization features. Implement monitoring systems and maintain clear ownership for model reliability. Required 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). 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 KPIs via ML-powered personalization. Experience with A/B testing platforms and experiment logging best practices. 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: Effective with engineers, PMs, and analysts. Analytical Thinking: Strong data-driven decision making and test methodologies. Ownership Mentality: Post-deployment ownership and continuous improvement. Execution-Oriented: Production-grade delivery with rigor. Curious & Innovative: Keeping up with ML advances for personalization. Compensation & Benefits
Base salary: $130,000$250,000 per year, paid semi-monthly. Total compensation includes base salary, profit-sharing bonus, and competitive benefits. Compensation varies by market rates and internal framework. Future increases are based on company and personal performance. About You
Passionate about building scalable ML systems for personalization and delivering measurable business impact. Equal Opportunity
Launch Potato is an Equal Employment Opportunity employer. We value diversity, equity, and inclusion. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy or related medical conditions), sexual orientation, gender identity or expression, age, veteran status, disability, or other protected characteristics. Location & Status
Remote-first with opportunities in Newark, NJ and other locations. Seniority level: Mid-Senior level. Employment type: Full-time. Job function: Engineering and Information Technology. Industries: Advertising Services. #J-18808-Ljbffr