Uber
Consumer · Rideshare
Uber is actively hiring for 130 AI-related roles, with a significant focus on agents, which accounts for 40% of their open positions. Application roles also represent a substantial portion of their AI hiring at 29%. The majority of these roles are within Engineering, with the United States being the primary hiring country. Frequent technology tags include model serving, recommender systems, and agent orchestration, suggesting a direction towards deploying and managing AI systems.
Currently tracking 25 active AI roles, down 34% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $142k–$336k (avg $220k).
Consumer · Rideshare
Uber currently has 36 active AI-related roles in our index. The most common open titles are: Engineering Manager II, AV Labs, Engineering Manager II, Evaluation & Simulation - AV Labs, Engineering Manager II, Ranking and Recommendations, Grocery and Retail, Enterprise Applications Developer, Group Product Manager, AV Labs. Most positions are in Engineering and Product.
Uber's active AI hiring is concentrated in: agents (50%), data (19%), evaluation (11%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Uber is hiring AI talent in: United States (33 roles), Netherlands (2 roles), India (1 role).
Job postings at Uber most frequently mention: Machine Learning, Autonomous Driving, Computer Vision, Robotics, LLM Evaluation & Grading.
In the past 30 days, Uber has posted 10 new AI-related roles. That is a -69% change versus the prior 30 days (32 → 10).
| Title | Stage | AI score |
|---|---|---|
| Director, Tech Transformation, Field Operations Director of Tech Transformation for Field Operations at Uber, focusing on integrating GenAI and automation into global customer service operations. The role involves co-developing and executing a technology roadmap, driving adoption of AI-powered solutions like agent assist (summarization, translation), redesigning workflows for AI integration, and leading automation initiatives to improve productivity, quality, and cost-efficiency. The goal is to transform FieldOps into a model tech-enabled organization by embedding technology into the operating model and fostering a culture of tech-first problem-solving. | Agent | 7 |