Machine Learning Engineer - Maps

Requirements

• ML modeling expertise: Experience with Deep Learning modeling using tools like PyTorch
• Large-scale data systems: Comfortable with processing 100s of terabytes of data with Spark
• Production-level ML: Can own training and serving pipelines for ML models that process 100k+ requests per second
• Commercial awareness: Can reason about business outcomes and their relationships with ML metrics

Nice to Haves

• ML experience in the maps domain
• Experience with real-time data processing using Flink

What You'll Be Doing

• Design and train Deep Learning models for traffic prediction
• Build Spark/Flink data pipelines for model training and real-time serving across 100+ cities
• Deploy and monitor ML models in production and A/B test model improvements
• Collaborate with partnering teams (Routing, Fares, AV) on ML-driven projects

Perks and Benefits

• Lead ML-driven traffic prediction systems powering Uber's global ETA accuracy (impacts millions of trips daily)
• Build and deploy real-time machine learning models (DeepETT) for multi-modal traffic forecasting across 100+ cities
• Drive ~$50m+/year gross bookings impact through traffic prediction improvements
• Uber's mission to reimagine the way the world moves for the better
• Opportunities for growth and collaboration in Uber's cultural office environments