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Senior ML Engineer, Recommendation Systems
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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. QUALIFICATIONS 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 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. OUTCOMES Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real‑time personalization with latency
