Machine Learning Engineer - Maps

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.

What the Candidate Will Do

• 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

Do you have experience in Spark?, * ML modeling expertise: Experience with Deep Learning modeling using tools like PyTorch

• Large-scale data systems: Comfortable with processing 100s of terrabytes 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., * ML experience in the maps domain.
• Experience with realtime data processing using Flink