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.

Senior ML Engineer role focused on building and deploying production-ready ML systems, including generative AI, computer vision, and personalization, to power core Uber products. The role involves the full ML lifecycle from experimentation to monitoring and emphasizes delivering end-to-end AI solutions.

Senior ML Engineer at Uber's Applied AI team, focused on delivering end-to-end AI solutions for core business problems, including Generative AI, Computer Vision, and Personalization. The role involves building production-ready ML systems and infrastructure, from experimentation to deployment and monitoring, to power user and business-facing products.

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.

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.

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.

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.

Uber is seeking a Staff Data Scientist for their Global Safety & Risk Team in Sao Paulo, Brazil. The role focuses on applying data analysis, machine learning, and statistical modeling to identify and prevent safety incidents. Key responsibilities include analyzing imbalanced datasets, designing and implementing binary classification models, generating insights from risk data, conducting complex experiments (Diff-in-Diff, Synthetic Controls), and defining success metrics. The role requires experience in building and deploying binary classification systems for high-stakes applications, experimental design beyond A/B testing, handling extreme class imbalance, and proficiency in Python and SQL. Experience with geospatial data analysis and real-time inference systems is preferred.

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.

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.

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.

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.

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.

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.

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.

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.

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 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 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.

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.

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 building prototype software solutions to automate business processes and drive operational efficiency using data and AI technologies. The candidate will leverage data to understand business problems, identify automation opportunities, conduct architecture and code reviews, and manage teams. The role involves developing end-to-end solutions for funnel optimization, user engagement, and user experience, and identifying use cases for machine learning and AI capabilities.

Senior Data Scientist role focused on fraud and risk detection at Uber. The role involves applying ML and LLM techniques to identify and mitigate fraud trends, developing new data products and models, and collaborating with cross-functional teams to influence strategy and balance fraud mitigation with user experience. Success requires strong technical and business acumen, with an emphasis on leadership, communication, and problem-solving.

Manager II, Science role at Uber focusing on end-to-end product development using data, analytics, modeling, and experimentation. The role involves formulating business problems, prototyping ML/optimization/causal inference models, generating data insights, and designing/analyzing experiments for Uber's mobility and logistics platforms, specifically within Delivery Marketplace and Uber One Membership teams. Requires leadership and mentoring of a team of scientists.

Staff Scientist role focused on end-to-end product development using data science, ML, optimization, and causal inference for Uber's marketplace platforms. Responsibilities include problem formulation, prototyping models, generating insights, designing and analyzing experiments, and providing thought leadership. The role emphasizes applying quantitative methods to improve marketplace efficiency, pricing, matching, and user experience across various Uber products.

This role focuses on end-to-end product development using data, analytics, modeling, and experimentation. It involves formulating business problems, prototyping ML, optimization, and causal inference models, generating data insights, and designing/analyzing experiments. The role spans various teams within Uber's mobility and logistics platforms, including Delivery Marketplace, Maps, Uber One Membership, Experimentation Platform, and Autonomous Mobility & Delivery.

Scientist II role focused on end-to-end product development using ML, Optimization, and Causal Inference for Uber's marketplace platforms. Responsibilities include formulating business problems, prototyping models, generating data insights, and designing/analyzing experiments. The role spans various teams like Delivery Marketplace, Maps, Uber One Membership, Experimentation Platform, and Autonomous Mobility & Delivery.

Scientist II role focused on improving the reliability, efficiency, and adoption of Uber's Reserve product by transitioning from heuristic-based decision making to building machine learning models for real-time decisions. This involves designing roadmaps, running large-scale experiments, and analyzing user behavior trends.