Lead ML Engineer, Recommendation Systems

WHO ARE WE?Launch Potato is a profitable digital media company that reaches over 30M+ monthly 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.WHY JOIN US?At Launch Potato, you’ll accelerate your career by owning outcomes, moving fast, and driving impact with a global team of high-performers.We convert audience attention into action through data, machine learning, and continuous optimization.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.MUST HAVEYou’ve shipped large-scale ML systems into production that power personalization at scale. You’re fluent in ranking algorithms and know how to turn data into engagement and conversions. Specifically:7+ 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)Skilled with 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 practicesYOUR ROLEYour mission: Drive business growth by building and optimizing the recommendation systems that personalize experience for millions of users daily. You’ll own the modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful.OUTCOMESBuild 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 latency 50ms across key product surfacesRun 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 reliabilityCOMPETENCIESTechnical Mastery: You know ML architecture, deployment, and tradeoffs inside outExperimentation Infrastructure: You set up systems for rapid testing and retraining (MLflow, WB)Impact-Driven: You design models that move revenue, retention, or engagementCollaborative: You thrive working with engineers, PMs, and analysts to scope featuresAnalytical Thinking: You break down data trends and design rigorous test methodologiesOwnership Mentality: You own your models post-deployment and continuously improve themExecution-Oriented: You deliver production-grade systems quickly without sacrificing rigorCurious Innovative: You stay on top of ML advances and apply them to personalizationWant to accelerate your career? Apply now!Since day one, we've been committed to having a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company. We value diversity, equity, and inclusion. We 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, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.Originally posted on Himalayas

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