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 95 active AI roles, down 34% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $142k–$336k (avg $224k).
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 |
| Staff Software Engineer Staff Software Engineer to lead the design and development of systems at the intersection of Security and AI, focusing on proactive detection, prevention, and response to security and privacy risks. The role involves owning critical architecture, driving complex initiatives, and setting engineering standards, with a focus on leveraging ML/AI techniques to improve security signal quality and scale detection capabilities. | Agent | 7 |
| Engineering Manager II - AI & Security Engineering Manager II to lead a team at the intersection of Security and AI. The team builds systems that proactively detect, prevent, and respond to security and privacy risks across Uber's platform. The role involves defining strategy, scaling systems, and leading a multidisciplinary team of engineers and applied scientists to secure modern application stacks, data systems, and emerging AI-powered products. Key responsibilities include leading and growing the team, defining the roadmap for security and AI-driven systems, building intelligent systems for risk automation, securing AI-powered applications against threats, partnering with other teams to embed security controls, and scaling systems to improve security posture. | Agent | 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 |
| Senior Scientist, Maps Senior Scientist role focused on developing and deploying machine learning models for Uber's Maps products, including location search, route optimization, and travel time predictions. The role involves data analysis, experimental design, and building data-driven product enhancements. | Agent | 7 |
| Staff Applied Scientist - Observability Uber is seeking an experienced Applied Scientist to build a real-time data platform for customer experience observability and analytics. The role involves designing and improving anomaly detection and alerting for multivariate time series, building methods to reduce incident impact, and contributing to intelligent incident response workflows. It also includes developing statistical monitoring for code deployment and feature rollout safety, and enabling analytics through data infrastructure. The scientist will define success metrics for incident detection systems and create evaluation harnesses. This is a high-impact role collaborating with engineering to drive an ambitious observability platform. | Eval GateAgent | 7 |
| Senior Machine Learning Engineer - Maps Senior ML Engineer at Uber's Places Data Team, focusing on building production ML systems for places matching, attribute inference, summarization, and friction detection using classical ML, deep learning, and generative AI. Responsibilities include end-to-end ML solution development, experimentation, and mentoring junior engineers. | 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 |