Uber is seeking a Sr Staff Agentic Systems Engineer to build the infrastructure for Agentic Dev Environments (ADEs), where AI agents are first-class participants in the software development lifecycle. The role involves designing and shipping the skill pack framework, building the multi-agent runtime with persistent background agents and swarm orchestration, developing AI-powered code intelligence, and owning the MCP platform layer for unified context infrastructure. The engineer will set technical direction for agentic infrastructure, define architecture and trust models, and mentor engineers in agentic fluency, working at Uber's scale across multiple monorepos and microservices.

Senior Staff ML Engineer for Uber's Moonshot AI team, focusing on building an enterprise AI data platform. Responsibilities include marketplace optimization, custom model development for annotation workflows (audio, video, text), automated quality evaluation using GenAI/LLM-as-Judge, and ML research. The role involves end-to-end ownership from research to production, technical leadership, and mentoring.

Principal ML Engineer for Uber's AV Labs, focusing on Physical AI. The role involves building advanced autonomy algorithms and models to add semantics to driving data, enabling better data mining, scene understanding, and causal modeling of vehicle behavior. The primary focus is on enriching L4 data for an evaluation engine, with a secondary focus on agentic systems.

This role focuses on AI Red Teaming and Model Risk for LLMs and agentic AI systems. The scientist will design and execute experiments to uncover unsafe or harmful behaviors, develop evaluation frameworks, and define risk metrics beyond standard accuracy. They will analyze agent workflows and collaborate with security and platform teams to implement guardrails and mitigations. The role requires experience with LLMs, adversarial evaluation, and analyzing complex model behavior.

Staff ML Engineer to architect, productionize, and scale an autonomous support agent that resolves customer issues end-to-end, focusing on LLM orchestration, evaluation, safety guardrails, and cost efficiency.

Uber is seeking a Staff Security Engineer to join the Cyber Defense team, focusing on developing next-generation security platforms that leverage AI/ML/GenAI. The role involves architecting and implementing an AI-orchestrated attack platform and autonomous security agents for threat hunting and incident response, as well as defining secure environments for AI model development. The position requires strong software engineering and distributed systems expertise, with a focus on building scalable, high-availability security solutions.

Lead Applied AI role focused on architecting and scaling intelligent automation solutions for Uber Advertising's Measurement Science function. The role involves building production AI applications end-to-end, designing AI agents and RAG systems, establishing AI safety and reliability practices, and building evaluation infrastructure. Requires strong Python, SQL, and experience with AI orchestration frameworks and LLM integration.

This role focuses on developing algorithms and foundation models for Physical AI in autonomous vehicles. The primary goal is to extract high-fidelity semantic meaning from complex urban edge cases to enrich L4 data lakes, optimize dataset quality for ML acceleration through advanced sensor data processing and auto-labeling, and implement scalable ML systems. The role involves collaboration with platform, product, and security engineering teams.

Senior Software Engineer for Uber's AV Labs, focusing on Physical AI to build autonomy algorithms that extract semantic meaning from driving data for L4 data lake enrichment. Responsibilities include algorithm development, systems architecture, technical leadership, and cross-functional collaboration with ML and platform teams.

Senior Software Engineer at Uber focused on designing, developing, and deploying Machine Learning models for ranking and recommendation systems. The role involves building real-time data pipelines, feature engineering, model optimization, and optimizing inference for large deep learning models in a large-scale, real-world application context.

Applied Scientist at Uber focused on building and productionizing ML/AI solutions for identity fraud detection and prevention on the Uber platform. The role involves applying various ML algorithms, including LLMs and generative AI, NLP, and time-series forecasting, to protect the platform at scale.

Senior ML Engineer at Uber's AV Labs focused on Physical AI, developing autonomy algorithms and models to add semantics to driving data, enrich L4 data lake, and optimize datasets for ML technologies in the autonomous driving domain.

Principal Engineer role focused on leading ML innovation in Uber's Driver Pricing organization. The role involves architecting and building next-generation ML systems for real-time pricing optimization, supply-demand balancing, and driver behavior modeling at scale. Key responsibilities include technical leadership, driving research in causal ML, reinforcement learning, and algorithmic game theory, owning the end-to-end ML model lifecycle, and building scalable ML infrastructure. The role requires expertise in modern ML frameworks, distributed computing, and various ML areas, with a strong emphasis on production deployment and measurable business impact.

Engineering Manager II for Computer Vision within Applied AI at Uber, leading a team to develop state-of-the-art vision and multimodal systems for production applications like document intelligence and onboarding automation. Responsibilities include driving technical strategy, managing engineers, and ensuring delivery of scalable AI solutions.

Engineering Manager role focused on leading a team to design, develop, and productionize advanced ML models and pricing algorithms for Uber's Marketplace. The role involves real-time multi-objective optimizations, deep learning, causal modeling, and reinforcement learning for large-scale distributed systems serving billions of trips, with a focus on providing earnings opportunities for drivers.

Senior Staff Engineer to architect, productionize, and scale an autonomous support agent for Uber's Customer Obsession team, focusing on mobile, web, and voice channels. The role involves end-to-end agent architecture, shipping production systems with high reliability, leading voice agent initiatives, advancing retrieval and reasoning pipelines, establishing evaluation frameworks, driving automation, and mentoring engineers.

The Applied AI team at Uber is seeking a Sr Staff ML Engineer to define the multi-year technical vision and architecture for foundation models powering discovery experiences across Mobility and Delivery. This role will focus on building a common semantic backbone for understanding users, places, merchants, items, and behavioral patterns, which will power personalization, search, agents, automation, and decision systems. The engineer will set long-term technical direction, drive alignment on strategy, and deliver measurable impact at global scale.

Staff ML Engineer to define and lead the foundation model strategy for AI-native discovery experiences across Uber's Mobility and Delivery platforms, focusing on Search, Recommendations, and Conversational AI. The role involves end-to-end technical strategy, architecture decisions, leading cross-team initiatives, defining investment areas (build vs fine-tune vs partner), and mentoring senior engineers.

Uber's ML Serving team is seeking a Senior Software Engineer to build and operate large-scale, low-latency systems for real-time ML and generative AI inference. The role involves leading the design and ownership of critical serving services and frameworks, partnering with ML engineers to productionize models, and improving system reliability and performance.

This role focuses on enhancing the Rider app's personalization AIML experience by leveraging data science and analysis to measure and optimize recommendation systems. The candidate will define and build metrics, ensure explainability and observability, design and analyze experiments, and design and build agentic AI to automate data science workflows. The goal is to improve user satisfaction and drive business growth through impactful personalization.

The role focuses on rider personalization and ranking systems, implying the development and deployment of AI/ML models to improve user experience and engagement within Uber's consumer platform.

The role focuses on algorithmic decisions, experimentation, measurement, and data strategy for Uber's global airports, specifically within the Matching group. The primary responsibility is to build systems that determine the optimal way to fulfill trips by deciding which earners to send offers to and when. This involves developing data-driven insights, designing and analyzing experiments, and contributing to the development of optimization algorithms and ML models for mobility matching at Uber's scale.

Staff Software Engineer with advanced ML expertise to join the Data Governance team within Engineering Security. Responsible for designing, developing, and managing distributed systems to protect large-scale data infrastructure, specifically focusing on detecting and addressing data security threats related to AI agents. This role involves building user-facing products, managing high-throughput systems, and developing intelligent fraud prevention strategies using AI and Data Security techniques, with a technical leadership component.

Scientist II role focused on improving the Reserve product by transitioning from heuristic-based decision making to building machine learning models for real-time decisions, enhancing driver and rider experience, and piloting new use cases. The role involves designing roadmaps, running large-scale experiments, and analyzing business/user behavior trends.

Software Engineer II on Uber's ML Training team, responsible for designing and building core components of large-scale distributed training systems for multi-GPU/TPU environments within the Michelangelo AI platform. Focuses on ML infrastructure and enabling efficient, reliable, and scalable model development.

This role focuses on building and evaluating AI booking agents and related ML systems for Uber's core mobility business. It involves designing experiments, developing models, and analyzing user behavior to improve the rider experience and drive growth. The role also includes shaping strategic vision, establishing metrics, and leading cross-functional initiatives.

The Reservations data science team at Uber is seeking a Staff Scientist to own the experience and algorithms powering the Uber Reserve Product. This role involves optimizing user experience, matching, dispatch, and pricing algorithms by transitioning from heuristic-based decision making to building and deploying machine learning models. The focus is on driving efficiency, improving unit economics, and increasing adoption and growth through data science methodologies, causal inference, large-scale experiments, and presenting findings to executive audiences. The role requires strong programming skills, MLOps practices, and experience in experimental design and analysis.

This role focuses on building and scaling end-to-end localization workflows that integrate ML/LLM systems into Uber's global platform. It involves evaluating and operationalizing MT and LLM solutions, partnering with Engineering and MLOps to translate experimental AI into production-grade systems, and driving continuous improvement through quality frameworks.

Software Engineer II at Uber focused on designing, building, and productionizing Machine Learning models and optimization engines. The role involves developing ML systems, improving model performance, and delivering business impact through algorithmic solutions in a production environment.

Software Engineer II role at Uber focused on designing, building, and operating end-to-end features for the AdTech tech stack. This involves creating scalable data pipelines using big data technologies, optimizing ad spend through experimentation, and productionizing ML models. The role requires collaboration with cross-functional teams and leveraging internal Uber technologies like Michelangelo (ML) and Piper (Data orchestrator).

This role focuses on building and scaling large-scale simulation platforms for autonomous vehicle (AV) testing and validation. It involves designing high-fidelity simulation frameworks, integrating sensor data and behavioral models, defining evaluation metrics, and generating edge-case scenarios. The goal is to accelerate AI research and ensure safety benchmarking for AVs.

This role focuses on driving AI-powered sales execution by identifying, designing, and testing AI-led workflows to improve seller productivity and merchant outcomes. It involves early-stage experimentation and validation of agentic, automated commercial systems, partnering with Product and Engineering, and evaluating AI tools.

This role focuses on building and evaluating models to predict and mitigate road safety risks using telematics and other data. It involves designing and executing experiments to test the effectiveness of these models and evaluations, working cross-functionally with product and operations teams.

Develop and improve GenAI-powered AI agents for customer personas, applying ML, causal inference, and experimentation to drive business impact. Collaborate with engineering and stakeholders, ensuring data quality and communicating insights.

Senior Machine Learning Engineer role focused on personalizing the Uber rider experience through ML models for product recommendations and merchandising. The role involves defining and driving ML solutions, providing technical leadership, and improving ML engineering best practices. It requires experience with ML frameworks, data pipelines, and applying ML to real-world problems like recommender systems.

Staff ML Engineer at Uber focused on Rider Intelligence, responsible for defining and executing technical strategies, leading the design, development, and production of end-to-end ML solutions for large-scale distributed systems, and mentoring a team of MLEs. Requires significant experience in ML model deployment and expertise in areas like search, recommendation systems, and ranking.

Software Engineer I role at Uber focused on building a GenAI Bot for customer support, involving design, development, and testing of software applications, with a focus on integrating AI components into the existing system.

Scientist II role focused on developing and analyzing ML models and optimization algorithms for Uber's mobility matching system, involving experimental design, causal inference, and large-scale data analysis to improve trip fulfillment and ETAs.

This role focuses on building AI-driven software factories to automate engineering work by orchestrating agents and workflows using LLMs and CLIs. It also involves designing prompting and validation loops for AI-generated output within the Uber Direct Logistics team.

Product Manager for Uber's AV Labs, focusing on developing platforms and systems to generate high-quality autonomous driving data from real-world operations. The role involves defining product requirements, driving execution, and working with engineering, science, and operations teams to accelerate AV capabilities, particularly in areas like machine learning and semantic understanding.

Group Product Manager for Uber's AV Labs, focusing on defining product strategy and roadmaps for technical domains including machine learning and data/validation platforms to accelerate the autonomous technology ecosystem. This role involves translating business goals into product direction, partnering with cross-functional teams, and influencing executive stakeholders.

Engineering Manager II for Uber's AV Labs, focusing on defining the roadmap, metrics, and technical architecture for autonomous vehicle evaluation and simulation. The role involves building platforms to leverage real-world driving data for AV development and managing a team to achieve high technical excellence in this domain.

Engineering Manager for Uber Eats Shopping Ranking & Personalization team, responsible for leading a team that powers personalized content and ranking across Storefront, Cart, and Checkout. Owns both user-facing personalization strategy and the underlying ranking platform, including scoring and serving decisions at scale. Partners with Data Science and MLE teams to productionize Deep Learning, GenAI, and embedding-based models. Focuses on building scalable platforms and architectural foundations for ranking and personalization surfaces.

Manager II, Science - Earner (Economics) at Uber. This role involves leading a team of scientists to develop and implement ML solutions for enhancing the earner experience, refining ambiguous questions, developing experimental designs, and ensuring data governance. The focus is on complex product and lifecycle analysis, experimentation, measurement foundations, and modeling within the earner journey.

Senior Software Engineer to build and scale an AI-powered customer engagement platform using LLM prompting and backend services to deliver conversational assistants for millions of users.

Staff Software Engineer role focused on developing and operating security services and frameworks using ML/GenAI for Uber's products and platforms. Responsibilities include building ML-powered security systems for detection, classification, and risk scoring, developing backend infrastructure and ETL pipelines for security analytics, and productionizing ML models for security use cases. Requires experience with distributed systems, machine learning, and Golang/SQL/Python.

This role focuses on applying machine learning and statistical modeling to identify and mitigate platform fraud, specifically related to user identity, to ensure platform trust and security. The Data Scientist will collaborate with product and engineering teams to develop and deploy anti-fraud strategies and reporting functions.

This role focuses on building and maintaining the knowledge base for customer-facing AI agents, ensuring they provide accurate and empathetic responses. It involves authoring prompt-aware content, structuring information for AI reasoning, tuning AI performance, and collaborating with product and engineering teams. The role requires a blend of technical writing, UX design, and prompt engineering skills, with a focus on optimizing retrieval accuracy and AI response patterns.