Lead Machine Learning Engineer, Recommendation Systems

Launch Potato

full-time

Posted on: 9/8/2025

Origin: • 💃 Anywhere in Latin America

✨ AI Apply

Senior

Amazon RedshiftBigQueryPythonPyTorchRaySparkSQLTensorflow

About the role

Drive business growth by building and optimizing recommendation systems that personalize experience for millions of users daily
Own modeling, feature engineering, data pipelines, and experimentation for personalization
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 <50ms across key product surfaces
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

7+ 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
Technical mastery of ML architecture, deployment, and tradeoffs
Experience setting up experimentation infrastructure (MLflow, W&B)
Strong analytical thinking and experimentation design
Collaborative cross-functional experience with engineers, PMs, and analysts