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 to define and scale Uber's AI-powered customer engagement platform, focusing on conversational assistants. This role involves setting technical vision, leading initiatives for GenAI assistants, owning platform components like orchestration and guardrails, and driving cross-org alignment for production-ready implementations at scale. Requires expertise in large-scale distributed systems and experience with GenAI/LLM systems in production.

Uber is seeking a Staff Machine Learning Engineer to design, build, and productionize ML models for ranking and recommendation systems, with a focus on Generative AI. The role involves working across the ML lifecycle, from infrastructure to model development and deployment, and collaborating with product teams.

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.

Senior Software Engineer role focused on building and productionizing scalable real-time ML systems for dynamic pricing and marketplace optimization at Uber. Requires expertise in deep learning, optimization algorithms, and ML frameworks, with a focus on data and experiment-driven model development.

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.

This role focuses on building the data integrity layer for an L4 autonomous driving data platform, validating and safeguarding the quality of multi-modal sensor streams. It bridges raw robotics telemetry and downstream machine learning, ensuring systems learn from ground truth.

Staff Machine Learning Engineer at Uber Marketplace focused on optimizing rider & driver matching using optimization, machine learning, and causal inference. The role involves building scalable ML libraries and systems, improving the ML Platform ecosystem, and collaborating with the ML community. Requires PhD or equivalent, 5+ years of experience, and proficiency in modern ML algorithms and frameworks.

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.

This role focuses on building ML models and AI agents for Uber's fares platform, which processes over $190B in gross bookings annually. The scientist will leverage experiment design, causal inference, and optimization techniques to solve problems in pricing, policy design, and defect reduction, influencing product strategy and automating data science workflows.

Machine Learning Engineer II at UberEats Feed, focusing on building and productionizing state-of-the-art recommendation models and large-scale ML systems. The role involves improving model quality, serving foundations, and data foundations for the UberEats Feed, which is the front door for users and merchants. Requires expertise in deep learning, recommendation systems, or optimization algorithms, and experience with ML frameworks and productionizing ML systems.

Software Engineer II ML on the Merchant Intelligence team at Uber Eats, focusing on building and productionizing ML models and scalable ML pipelines for merchant data processing, entity resolution, and feature engineering. The role involves improving data quality and serving critical use cases like sales outreach, onboarding, ads, and feed optimization.

Machine Learning Engineer II at UberEats Feed, focusing on innovating and productionizing state-of-the-art recommendation models and building end-to-end large-scale ML systems for the HomeFeed Recommendation. The role involves improving ML quality, model serving, and data foundations.

This role focuses on developing and productionizing AI/ML models for Uber's core security engineering, specifically in access management, identity, and authorization. The engineer will translate security needs into AI-first solutions, integrate ML systems, and mentor junior engineers. Requires strong experience in ML model development, productionization, and familiarity with security contexts.

Senior Staff Engineer, Tech Lead for Uber's Matching & Segmentation organization, focusing on ML-powered systems for real-time rider-driver matching and marketplace segmentation. The role involves architecting, developing, and deploying ML and optimization systems at scale, leading cross-org initiatives, and mentoring engineers. Experience with backend development, distributed systems, ML systems, and optimization algorithms is required.

Sr. Engineering Manager to lead multiple teams building and operating ML-powered matching, segmentation, and marketplace optimization systems at scale for Uber's mobility marketplace. The role involves defining technical vision, cross-functional partnership, and people management, with a focus on real-time algorithms and large-scale online experiments.

Senior Machine Learning Engineer at Uber focused on Rider Pricing & Incentives. The role involves developing and implementing advanced ML and optimization techniques to drive revenue and ridership growth. Key responsibilities include improving pricing and promotion algorithms, owning the end-to-end problem, and mentoring junior team members. Requires expertise in deep learning, generative AI, causal modeling, and reinforcement learning, applied to large-scale data systems.

Senior Machine Learning Engineer at Uber focused on Rider Pricing & Incentives. The role involves developing and implementing advanced ML and optimization techniques to optimize pricing strategies and promotional systems, driving revenue and ridership growth. It requires end-to-end ownership, collaboration with cross-functional teams, and mentoring junior members. The role utilizes deep learning, generative AI, causal modeling, and reinforcement learning.

Staff Machine Learning Engineer at Uber focused on Rider Pricing & Incentives. The role involves optimizing pricing and promotion algorithms using advanced ML techniques like deep learning, generative AI, causal modeling, and reinforcement learning. Responsibilities include leading a team, improving model performance, owning the end-to-end problem, and mentoring junior members. The role requires experience with ML and optimization algorithms, large-scale data systems, and production-ready algorithmic systems.

Engineering Manager II, Machine Learning for Uber's Rider Pricing & Incentives team. This role involves managing a team of SWEs and MLEs to develop and implement ML and optimization techniques for pricing and promotions, impacting billions of rides globally. Responsibilities include improving model performance, owning the end-to-end product/technical roadmap, and mentoring junior team members. Requires a Masters degree in a related field with 7+ years of experience, proficiency in programming languages, and experience with ML and optimization algorithms. Preferred qualifications include a PhD, experience with large-scale data systems, deep learning, generative AI, causal modeling, and reinforcement learning.

Senior Machine Learning Engineer to design and scale technical foundations for Uber's driver incentive systems. Develop and productionize large-scale ML models and decision systems for incentive generation and delivery. Collaborate with cross-functional teams to shape marketplace efficiency and driver earnings.

Software Engineer to join the Matching ML Platform team, focusing on building and scaling a low-latency platform for real-time matching decisions, evolving the ML platform for inference and experimentation, and designing extensible architectures for ML-powered matching capabilities.

Senior Software Engineer role focused on developing and operating security services and frameworks using ML/GenAI techniques to protect Uber's products and platforms. Responsibilities include building ML-powered security systems, developing backend infrastructure and ETL pipelines, and productionizing ML models for security use cases.

Uber's Marketplace is at the heart of Uber’s business and the Dynamic Supply Pricing (DSP) team develops the models, algorithms, signals, and large-scale distributed systems that power real-time driver pricing for billions of rides. Engineers on the team work on cutting-edge marketplace ML problems and real-time multi-objective optimizations serving 1M+ predictions/second. They regularly present $1B+ opportunities to executive stakeholders and receive close mentorship from the most senior engineers within the organization, setting you up for fast-tracked career growth and the opportunity to learn from experienced technical leaders. We are looking for exceptional ML engineers with a track record of extraordinary impact and with a passion for building large-scale systems that optimize multi-sided real-time marketplaces. In this role, you will lead the design, development, and productionization of advanced ML models and pricing algorithms, covering deep learning, causal modeling, and reinforcement learning. You will work with engineers, product managers, and scientists to set the team’s technical direction and solve some of Uber’s most challenging and most complex business problems in order to provide earnings opportunities for millions of drivers worldwide.

Develops and analyzes ML models and optimization algorithms for Uber's mobility matching system, focusing on improving trip fulfillment efficiency and reliability. Requires strong quantitative skills, experimental design, and causal inference.

Software Engineer II on the Uber Agent Platform team, focused on building the foundational platform for AI agents that run parts of the business. This includes tools and infrastructure for creating, evaluating, debugging, and deploying multi-agent systems at scale, with an emphasis on systems design, evaluation frameworks, managed services, and developer workflows.

Staff Machine Learning Engineer at Uber on the Marketplace Signals team, focused on developing and optimizing ML models for marketplace signals like ETA predictions, supply availability, and demand forecasts. The role involves building scalable systems for these signals, leveraging ML techniques, and working with real-time data and distributed systems. Requires a strong background in ML, statistics, optimization, and experience with ML frameworks and data pipelines.

ML Engineer role focused on building and deploying ML models for risk-adaptive, real-time security decisions within Uber's Zero Trust Architecture. The role involves framing security problems as ML tasks, feature engineering, model development, and production deployment.

Staff Software Engineer on Uber's ML Serving team within the AI Platform, focusing on infrastructure for real-time ML and generative AI inference at scale. Responsibilities include defining technical direction, leading cross-team initiatives, and designing foundational architectures for thousands of models in production.

Software Engineer II role focused on building backend systems for automated retail intelligence, processing image data, orchestrating model inference, and converting predictions into inventory signals. Requires experience with scalable data pipelines, backend service development, and system reliability.

Product Manager for Earner Intelligence at Uber, focusing on building ML products and platforms for efficient growth and user experience optimization for drivers and couriers. The role involves owning the product roadmap, defining strategy, and working with ML techniques like causal ML, supervised ML, multi-armed bandits, genAI LLM, and deep learning embeddings. Key responsibilities include understanding earner behavior, designing recommendation engines and matching algorithms, and driving the execution of ML-based projects from data collection to model training and observability.

Senior Machine Learning Engineer at Uber Direct, focusing on building and productionizing ML systems for real-time logistics operations, including ETA prediction, demand forecasting, and dispatch optimization. The role involves the end-to-end ML lifecycle, from data exploration to deployment and monitoring, with an emphasis on scalable ML systems and driving business impact.

Seeking a Solutions Architect Manager to lead pre-sales engagements and define product roadmaps for Uber AI Solutions, focusing on enterprise AI data journeys in areas like Autonomous Systems, Robotics, and Generative AI. The role involves building a new Solutions Architecture function, mentoring a team, and ensuring margin-positive solutions from data to impact.

Senior ML Engineer role focused on building AI-driven security systems for Uber's Zero Trust Architecture. The role involves translating security needs into ML problems, developing and productionizing ML models for real-time risk-adaptive decisions, and integrating these systems into critical access pathways. This is a greenfield opportunity at the intersection of ML, security, and infrastructure.

This role focuses on applying machine learning to enhance safety within Uber's platform. The responsibilities include owning the end-to-end applied science workflow, from problem scoping and deep-dive analysis to developing models and driving experimentation. The candidate will work cross-functionally with product, engineering, and operations to deliver impactful safety solutions and will be responsible for deploying sophisticated ML models into production, ensuring their robustness and measurable safety impact. Experience with LLMs in a production environment is preferred.