Uber
Consumer · Rideshare
Currently tracking 95 active AI roles, down 34% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $142k–$336k (avg $224k).
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.
Consumer · Rideshare
Uber currently has 86 active AI-related roles in our index. The most common open titles are: Senior Software Engineer (3), 2026 PhD Applied Research Project (3 months), Aarhus, 2026 PhD Research Intern, India, 2026 PhD Software Engineering Internship, Security, Amsterdam, Agentic GTM Lead. Most positions are in Engineering and Product.
Uber's active AI hiring is concentrated in: agents (50%), application (19%), data (15%). 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 (67 roles), India (12 roles), Netherlands (7 roles), Denmark (1 role).
Job postings at Uber most frequently mention: Machine Learning, Production ML Systems, Autonomous Driving, Robotics, Generative AI.
In the past 30 days, Uber has posted 5 new AI-related roles. That is a -84% change versus the prior 30 days (32 → 5).
| Title | Stage | AI score |
|---|---|---|
| Head of EMEA Risk Operations Lead Uber's regional fraud strategy and operational execution in EMEA, focusing on deploying and operationalizing AI-enabled tools to detect, investigate, and prevent fraud. This role involves shaping product roadmaps, building systems for proactive risk posture, and navigating complex regulatory environments. | Agent | 7 |
| Sr Applied Scientist Senior Applied Scientist at Uber to build and deploy ML/AI solutions in production, taking ideas from concept to real-world systems. The role involves end-to-end work from problem definition to production integration, focusing on classification, prediction, anomaly detection, and risk scoring. It also includes improving the reliability and robustness of AI systems, including LLM-based applications, and applying model adaptation techniques like fine-tuning. | Ship |
| 7 |
| 2026 PhD Software Engineering Internship, Security, Amsterdam PhD internship focused on AI-first security, researching and implementing AI agents to interact with security platforms, building models for identity risk assessment, and developing LLM-driven layers for vulnerability prioritization within Uber's production ecosystem. | Agent | 7 |
| 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 |
| Staff Scientist Staff Applied Scientist on the Earner team at Uber, focusing on building the best platform for drivers and couriers. The role involves setting science strategy for personalization, marketplace efficiency, reliability, and experimentation guardrails. Responsibilities include designing and analyzing large-scale experiments, building statistical, optimization, and machine learning models, defining metrics and observability, leading multi-team initiatives, advancing causal inference and optimization frameworks, mentoring scientists, and communicating with leadership. Requires an M.S. or Ph.D. in a quantitative field with 8+ years of industry experience, deep expertise in statistical inference, experimental design, causal inference, machine learning, optimization, and proficiency in Python and SQL with production-minded code quality. | Ship | 7 |