Machine Learning Engineer - Maps
Uber
About the Role
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
Basic Qualifications
- 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.
Preferred Qualifications
- ML experience in the maps domain.
- Experience with realtime data processing using Flink
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuelds progress. What moves us, moves the world - let’s move it forward, together.
Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.
*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to accommodations@uber.com.