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.

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 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.

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.

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.

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.

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.

This role focuses on designing, developing, optimizing, and productionizing machine learning (ML) or ML-based solutions and systems at scale for Uber's various platforms (Marketplace, Maps, Membership, AV). The engineer will also contribute to ML infrastructure for model development, training, deployment, and scaling. Key responsibilities include collaborating with stakeholders, writing efficient code for low-latency and high-reliability models, and implementing monitoring systems for live environments. The role emphasizes the full ML lifecycle, from development to productionization and monitoring, with a focus on scalable and reliable ML systems.

Software Engineer II focused on designing, developing, optimizing, and productionizing machine learning models and systems at scale for Uber's marketplace, maps, and membership platforms. Responsibilities include writing efficient code for low-latency, high-reliability models, implementing monitoring systems, and collaborating with cross-functional teams. Requires experience with the full ML lifecycle, including deployment and orchestration.

Staff ML Engineer for Uber's Consumer Incentives team, focusing on profitability and growth by enhancing customer experience through ML and optimization solutions. Responsibilities include technical leadership, designing and implementing ML systems, managing end-to-end project execution, and collaborating with cross-functional teams.

Senior Software Engineer to lead platform architecture for Uber AI Solutions, focusing on building foundational data infrastructure for next-generation AI systems. The role involves designing and delivering scalable backend solutions that integrate human expertise with ML for producing high-quality, model-ready datasets for multimodal and real-world AI use cases.

Senior Staff Machine Learning Engineer at Uber, focused on Ads. This role involves leading the design and implementation of advanced ML systems, owning the end-to-end ML model lifecycle from research to production, building scalable ML architecture, establishing best practices, and creating platform abstractions. The role also includes mentoring other engineers and collaborating with product and science teams.

Staff ML Engineer for Uber's Ads team, focusing on designing, building, and evolving ML systems for ads selection, ranking, pricing, and delivery. The role involves end-to-end ownership of ML systems, including training, feature infrastructure, and low-latency online inference, with a strong emphasis on improving model quality, serving efficiency, observability, and reliability. The position requires leadership in defining the technical roadmap, mentoring, and collaborating with product and infrastructure teams to drive significant impact on Uber's Ads business.

Develop and improve ML models for Uber's Ads recommendation and auction systems, focusing on user and merchant behavior to enhance ad relevance and pricing.

Senior Scientist role focused on optimizing merchant selection and pricing models for Uber's Delivery business. This involves leading data scientists, designing A/B experiments, collaborating with cross-functional teams, generating insights from large datasets, and productionizing models. Requires strong analytical skills, ML development experience, and leadership capabilities.

Director of Engineering for Uber's Marketplace team, leading a team of 40 engineers and applied scientists. The role focuses on building and leading the tech and systems for driver pricing and matching, including ML modeling, optimization, and backend systems. Requires strong management experience and a background in software engineering with ML/optimization.

Senior Program Manager for Uber AI Solutions, focusing on AI training and evaluations for robotics, computer vision, and related fields. The role involves client engagement, presales support, program delivery management, and driving innovation in AI training services. It requires experience in ML operations, data annotation, AI infrastructure, and client-facing service delivery.

Senior Staff ML Engineer focused on Account Integrity and fraud prevention using ML models. The role involves shaping the technical roadmap, designing and implementing end-to-end ML pipelines, and productionizing solutions at scale. Familiarity with multi-task learning, LLMs, and anomaly detection is preferred.

Machine Learning Engineer II focused on Dynamic Supply Pricing (DSP) at Uber, developing models, algorithms, and large-scale distributed systems for real-time driver pricing. The role involves designing, developing, and productionizing end-to-end ML solutions, including deep learning, causal modeling, and reinforcement learning, for a high-volume marketplace serving millions of drivers.

Staff ML Engineer role focused on developing and productionizing advanced ML models and pricing algorithms for Uber's Marketplace Pricing, involving deep learning, causal modeling, and reinforcement learning in large-scale distributed systems serving billions of trips.

Staff Machine Learning Engineer focused on pricing and incentives, designing and implementing ML models for dynamic pricing and incentive allocation at Uber scale. Responsibilities include shaping the technical roadmap, end-to-end ML pipeline ownership, and productionizing solutions.

Uber's Surge team is hiring a Staff ML Engineer focused on causal inference to build large-scale pricing optimization systems. The role involves training ML models with sparse data, designing experiments, and developing causal models for real-time marketplace conditions. This is a backend role with significant impact on rider experience and driver earnings.

Develops and analyzes ML models and optimization algorithms for Uber's mobility matching system, focusing on improving trip fulfillment, reliability, and ETAs. Collaborates with cross-functional teams to drive system development from concept to production.

Senior Staff Engineer role focused on Competitive Intelligence at Uber, involving extracting insights from market data and building defenses against scraping and data abuse. This includes adversarial machine learning and bot detection. The role requires leading technical vision, system design, and mentoring engineers, with a focus on high-stakes ambiguity and technical complexity.

Staff Scientist role focused on designing and building core algorithmic components for Uber's Advertising and Offers platform, involving statistical, optimization, and machine learning models for auction, bidding, pacing, and ranking. The role requires understanding system performance, leading algorithm design, and collaborating with cross-functional teams to productionize solutions.

Senior Engineering Manager for Uber Eats Shopping ML team, focusing on personalization and discovery experiences. The role involves defining strategy, collaborating with cross-functional teams, developing scalable ML systems, and staying updated on industry trends. Requires significant experience in managing ML teams and applying ML to real-world problems, particularly in consumer-facing products.

Lead Product Manager for Uber's Seller Tech team, focusing on the end-to-end commercial platform. This role involves owning product strategy, roadmap, and execution for a multi-product domain, managing a team of PMs, and leading the design and integration of AI and agentic capabilities into the product surface. The goal is to translate business goals into product strategy and requirements for platform investments, ensuring scalability and architectural soundness.

Software Engineer II on the Incentive Platform team at Uber, focusing on building and scaling a business-critical, ML-powered incentive ecosystem for drivers and couriers. The role involves architecting and developing high-throughput, real-time distributed systems and ML inference infrastructure to optimize incentive generation and delivery.

This role focuses on improving operational efficiency and delivery quality for Uber Direct, a merchant-focused delivery service. It involves strategic planning, data analysis, cross-functional collaboration, and project management to optimize key business metrics. While the role uses AI tooling and works with data, it does not involve building or shipping AI models.

This role focuses on analyzing data, designing experiments, and building models to optimize Uber's rider pricing and incentives algorithms and platform. It involves understanding product performance, developing new metrics, and informing strategies for marketplace efficiency and reliability.

This role focuses on optimizing the rider experience and driving growth in Uber's mobility business through data analysis, experimentation, and ML modeling. The scientist will develop AI systems to understand metric trends, optimize conversion funnels, and create personalized features and pricing models for riders.

This role focuses on optimizing Uber's marketplace at airports globally, using data to diagnose issues, evaluate changes, and improve reliability, conversion, and driver earnings. It involves cross-functional collaboration with operations, product, and engineering teams, and strengthening data foundations for airport performance reporting.

Staff Scientist role focused on identifying and solving complex business problems within the Airports and Travel segment at Uber. The role involves strategic problem finding, leading end-to-end data-driven solutions, advanced experimentation, defining business metrics, and influencing senior leadership. While the role mentions using AI/Agentic tools as a preferred qualification, its core function is not AI/ML development but rather applying data science and experimentation to business challenges.

This role focuses on optimizing employee experience with HR services through ServiceNow and AI-enabled self-service. The candidate will define strategy for leveraging ServiceNow, establish global knowledge standards for AI readiness, and drive AI-enabled initiatives like virtual agents and automated triage to enhance digital employee experience. The role also involves platform governance and ensuring compliance.