Senior Machine Learning Engineer, Recommendation Systems

Overview Senior Machine Learning Engineer, Recommendation Systems. We are hiring 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 and contribute to engagement, retention, and revenue at scale. Responsibilities Drive business growth by building and optimizing the 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) with efficiency and reliability improvements. Design ranking algorithms that balance 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. Partner 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). 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. Competencies Technical Mastery: You know ML architecture, deployment, and tradeoffs inside out. Experimentation Infrastructure: You set up systems for rapid testing and retraining (MLflow, Weights & Biases). Impact-Driven: You design models that move revenue, retention, or engagement. Collaborative: You thrive working with engineers, PMs, and analysts to scope features. Analytical Thinking: You break down data trends and design rigorous test methodologies. Ownership Mentality: You own your models post-deployment and continuously improve them. Execution-Oriented: You deliver production-grade systems quickly without sacrificing rigor. Curious & Innovative: You stay on top of ML advances and apply them to personalization. We’re 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, veteran status, disability status, or other legally protected characteristics. J-18808-Ljbffr

Location: Rio de Janeiro, Rio de Janeiro, BR

Posted Date: 9/11/2025