Lead Machine Learning Engineer, Recommendation Systems

4 weeks ago


Quebec, Canada Launch Potato Full time

Overview Lead Machine Learning Engineer, Recommendation Systems at Launch Potato. Our mission is to connect consumers with the world’s leading brands through data-driven content and technology. We are headquartered in South Florida with a remote-first team spanning over 15 countries, built on 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, serving 100M+ predictions daily and directly impacting engagement, retention, and revenue at scale. Must Have 7+ years building and scaling production ML systems with measurable business impact 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 experiences 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



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