Uber

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.

Senior Data Scientist role focused on applied AI and ML for Uber's Discovery Science team. The role involves designing and implementing ML models for recommender systems, unifying business interests, and applying advanced techniques like Deep Learning and Reinforcement Learning to solve complex problems. It requires end-to-end ownership of models, from hypothesis to production debugging in low-latency environments.

Machine Learning Engineer II at Uber on the Rider ML team, focused on developing and deploying ML infrastructure and ranking solutions for personalized rider experiences, optimizing homepage and product selection ranking with real-time, low-latency deep learning models.

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.

Uber is seeking a Sr. Machine Learning Engineer to enhance rider experience through personalized recommendations and tailored services at scale. The role involves developing and deploying deep learning models for real-time, ultra-low latency applications, focusing on ranking solutions and ML infrastructure for rider engagement across various touchpoints. The position requires strong software engineering skills, expertise in ML methodologies, and experience with ML frameworks and data pipelines.

Senior ML Engineer on the Content Platform team at Uber, focusing on building core ML capabilities for a customer support chatbot (Nova). The role involves improving response quality through observability, retrieval, ranking algorithms, and content strategies. Key responsibilities include defining observability and evaluation frameworks, developing retrieval and ranking algorithms (semantic search, RAG), building feedback loops, and collaborating with stakeholders to deliver production systems.

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.

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.

Senior Machine Learning Engineer role focused on building and productionizing Generative AI-powered ranking and recommendation systems for Uber's shopping platform. Requires strong ML experience, productionization skills, and expertise in big-data architecture and ML technologies.

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 Software Engineer to lead platform architecture for Uber AI Solutions, focusing on building foundational model data infrastructure that integrates human expertise with ML to produce 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.

Uber's Ads ML team is seeking a Senior Machine Learning Engineer to optimize ad recommendations and auction mechanisms within their ecosystem. The role involves designing and implementing ML models, developing scalable ML pipelines, applying advanced ML techniques for targeting and delivery, and collaborating with cross-functional teams. The goal is to enhance user engagement and merchant benefits through data-driven insights and robust ML solutions, directly impacting Uber's growing Ads business strategy.

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.

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.

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.

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.

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.

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.

Manager II, Science for Uber Ads & Offers team, focusing on Ads Delivery & Optimization. This role involves leading a team of scientists to design and implement algorithms for Ads relevance, ranking, bidding, and pacing, driving strategic vision and execution for the Ads delivery product. Requires experience in Ad tech or marketplace industries and building Ads systems.

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.