Overview
Senior Machine Learning Engineer, Recommendation Systems. Launch Potato is a profitable digital media company with brands such as FinanceBuzz, All About Cookies, and OnlyInYourState, reaching over 30M+ monthly visitors. We connect consumers with leading brands through data-driven content and technology. Headquartered in South Florida with a remote-first team spanning over 15 countries, we foster a high-growth, high-performance culture where speed, ownership, and measurable impact drive success. We hire 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, serving 100M+ predictions daily and directly impacting engagement, retention, and revenue at scale.
We are committed to having a diverse, inclusive team and culture and are proud to be an Equal Employment Opportunity company. We value diversity, equity, and inclusion.
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 make personalization smarter, faster, and more impactful.
Must Have / Qualifications
5+ years building and scaling production ML systems with measurable business impactExperience deploying ML systems serving 100M+ predictions dailyStrong 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 lakesFamiliarity with distributed computing (Spark, Ray) and LLM / AI Agent frameworksTrack record of improving business KPIs via ML-powered personalizationExperience with A / B testing platforms and experiment logging best practicesOutcomes
Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scaleEnhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvementsDesign ranking algorithms that balance relevance, diversity, and revenueDeliver real-time personalization with latencyRun statistically rigorous A / B tests to measure true business impactOptimize for latency, throughput, and cost efficiency in productionPartner with product, engineering, and analytics to launch high-impact personalization featuresImplement monitoring systems and maintain clear ownership for model reliabilityCompetencies
Technical Mastery : ML architecture, deployment, and tradeoffsExperimentation Infrastructure : MLflow, Weights & Biases (W&B) usageImpact-Driven : Models that move revenue, retention, or engagementCollaborative : Works with engineers, PMs, and analystsAnalytical Thinking : Designs rigorous test methodologiesOwnership Mentality : Post-deployment ownership and continuous improvementExecution-Oriented : Production-grade systems delivered quickly with rigorCurious & Innovative : Keeps up with ML advances in personalizationJob Details
Seniority level : Mid-Senior levelEmployment type : Full-timeJob function : Engineering and Information TechnologyIndustries : Advertising ServicesWe do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, protected veteran status, disability, or other legally protected characteristics.
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