Senior Machine Learning Engineer, Recommendation Systems

Overview Senior Machine Learning Engineer, Recommendation Systems at Launch Potato. Role focuses on building, deploying, and scaling personalization systems to power real-time recommendations across millions of user journeys. 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 impacting engagement, retention, and revenue at scale. 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 speed, relevance, 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 efficiency in production. Collaborate with product, engineering, and analytics to launch high-impact personalization features. Implement monitoring systems and maintain clear ownership for model reliability. 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). 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. Competencies Technical Mastery: ML architecture, deployment, and tradeoffs Experimentation Infrastructure: MLflow, W&B Impact-Driven: models that move revenue, retention, or engagement Collaborative: works with engineers, PMs, and analysts Analytical Thinking: data-driven testing methodologies Ownership Mentality: post-deployment model ownership and improvement Execution-Oriented: production-grade systems Curious & Innovative: staying current with ML advances Benefits & Culture We’re a diverse, inclusive team and equal employment opportunity employer. We value diversity, equity, and inclusion. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, gender identity, age, veteran status, disability, or other protected characteristics. Company Headquartered in South Florida with a remote-first team spanning over 15 countries. We’re a profitable digital media company reaching 30M monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState, focused on data-driven content and technology to connect consumers with leading brands. J-18808-Ljbffr

Location: Rio de Janeiro, Rio de Janeiro, BR

Posted Date: 10/13/2025