AI Jobs & Internships at Uber 2026
Uber has built a world-class ML platform (Michelangelo) and applies machine learning extensively across ETA prediction, dynamic pricing, fraud detection, driver-rider matching, and food delivery optimization. The company pioneered MLOps concepts through Michelangelo before that term existed, influencing how the entire industry thinks about production ML infrastructure. Uber's ML platform and applied science teams have published influential research that is widely cited in the ML community.
AI Roles at Uber
ML Engineer (Michelangelo)
Applied Scientist
Data Scientist
Pricing AI Engineer
ML Platform Engineer
Fraud ML Engineer
Maps AI Engineer
Feature Engineering Specialist
Work Culture at Uber
Uber's culture has matured significantly from its controversial early years under Travis Kalanick. The company now emphasizes integrity, inclusion, and sustainable growth under CEO Dara Khosrowshahi. The engineering culture remains technically excellent — Uber's ML teams have consistently published influential open-source contributions and research. The company operates across complex domains (marketplace optimization, geospatial analysis, regulatory environments) that provide intellectually interesting ML problems.
How to Get a Job at Uber
- 1.
Michelangelo is one of the most influential ML platform architectures — familiarity with their published design (including the Uber Engineering Blog posts) shows genuine interest
- 2.
Marketplace ML problems — dynamic pricing, two-sided matching, demand forecasting — are central to Uber's business; prepare to discuss these domains
- 3.
Uber's internship program is large and well-regarded, particularly for data science and ML engineering
- 4.
Experience with geospatial data, routing algorithms, or time series forecasting at scale is highly relevant given Uber's core business
- 5.
The interview process includes significant product case components for data science roles — prepare to design ML systems for ridesharing and delivery use cases