2026 PhD Software Engineer Intern (Machine Learning), United States

Job Description

We're looking for PhD candidates in the machine learning and optimization domain to intern with a variety of teams during summer 2026 (12 weeks). You will be embedded in an engineering team and work closely with other specialists, data scientists, and product managers. As a PhD intern, you will work on an exciting yet bold problem independently, under the supervision of an experienced engineer on that team.

About the Role

At Uber, we work on many ambitious engineering products covering many lines of business as well as the underlying platform technologies that power those businesses. We foster growth and increase profitability of Uber by pushing the frontiers of machine learning, constrained optimization, statistics, data science and economics and developing highly reliable and scalable platforms to accelerate Uber's impact on the transportation industry.

As a PhD software engineer intern, you will have a lot of opportunities to work with product managers, data scientists and, of course, engineers from different teams. You will have an opportunity to learn how to iterate over a product for greater success while demonstrating your area of expertise (machine learning, statistics, constrained optimization, distributed system, etc.). This is a unique opportunity to grow your skills with real-world experience and do highly impactful, yet fun work at the same time. It is an ambitious yet rewarding job!

Team Options

• The Delivery Marketplace: Consumer and Courier Pricing, Matching and Logistics is responsible for building the core technology across the delivery line of business including food delivery, grocery, and retail last mile. These include a diverse set of transformational projects and impactful products ranging from finding the optimal match between jobs and earners in the delivery space, to setting optimal prices for Eaters and Couriers. The organization is formed of talented teams of engineers and scientists working side by side on the state of the art innovations on the matching objective function, pricing algorithms, and promotions. To achieve these goals, we leverage big data tools and apply advanced machine learning models to drive top line and bottom line business outcomes.
• The Trusted Identity org inspires trust in the Uber business cycle through verified and authentic identities. The Account Integrity team prevents fake identities from operating on the platform, and ensures that accounts are not duplicated nor fraudulent to enhance accountability. The Identity Verification team ensures accounts are created by real humans by verifying their attributes. The Account Security team is responsible for defending account holders from fraud related to phishing and ATO (account takeover) attempts. The Device Intelligence team is responsible for collecting device signals that are critical for the other teams to reliably achieve their goals.
• The Uber Risk engineering team is tackling challenges related to fraud and abuse detection. Our work is crucial in maintaining the integrity of Uber's platform. The team employs advanced machine learning including LLMs and computer vision, knowledge graph, similarity search and reinforcement learning to monitor transactions, detect unusual patterns, and implement robust verification methods
• The Mobility Matching team at Uber plays a crucial role in developing and optimizing algorithms and systems that match supply (drivers) with demand (riders) in real-time. You will work on complex problems, leveraging data and build ML algorithms to ensure efficient and reliable marketplace matching. The team is broadly part of the Marketplace (PIMS) org, a central pillar to Uber's core technology which includes pricing, incentives/investments, matching, surge, etc. As the key brain of the company, we manage the complex dynamics of supply and demand, optimize dispatching algorithms, and continuously innovate to enhance the overall user experience for both riders and drivers. Improvements in these systems increase revenue in the hundreds of millions of dollars, and decrease wasted time of drivers and users. Your contributions will directly impact the experience of millions of users worldwide.
• The Rider Pricing and Incentives team at Uber plays a crucial role in developing and optimizing algorithms and systems that determine the trip prices, discounting and promotions for Uber riders worldwide. You will work on complex modeling, pricing and incentives targeting problems, leveraging data and building ML algorithms to ensure accurate and robust pricing models and successful pricing optimization outcomes. The team is broadly part of the Marketplace organization, a central pillar to Uber's core technology which more broadly includes pricing, incentives/investments, matching, surge, etc. As a key team within Uber, we manage the complex dynamics between short term optimized pricing and long-term optimized pricing, promotion targeting and continuously innovating to enhance the overall user experience for riders. Improvements in these systems increase revenue in the hundreds of millions of dollars, and positively impact the experience of millions of users worldwide, as well as their ability to get around in the world.
• Join our Capacity Planning team as a Ph.D. intern to drive the future of infrastructure resource management through advanced quantitative methods. You will be at the forefront of implementing our data-driven forecasting vision, moving away from reactive, manual processes to scalable, automated solutions that utilize predictive analytics and real-time utilization data. This is a high-impact opportunity to work with the Applied Science and Engineering teams on designing and optimizing the central intelligence systems for capacity planning. Your expertise will be crucial in developing granular, highly accurate resource forecasts, ensuring service reliability for unexpected demand, and making key decisions that directly influence financial accountability, operational agility, and optimal resource utilization across the entire platform.
• The Autonomous Vehicles Cloud team is dedicated to accelerating the autonomous vehicles industry to L4 by serving as a premier AV data leader. Recently, we announced a partnership with NVIDIA to jointly develop a data factory for autonomous vehicle development. The team includes engineers with diverse backgrounds in embedded software, cloud computing, batch data processing, and ML research. We are looking for talent passionate about autonomous vehicles and eager to make a significant impact.
• The Marketplace Signals team at Uber is responsible for building and optimizing foundational marketplace signals that power user experiences and drive marketplace efficiency. Our team ensures that key signals-such as eyeball ETA, spinner time, and supply reliability indicators-are leveraged effectively across various Uber products and levers, enabling data-driven decision-making and seamless coordination across different business functions.
• Applied AI is a horizontal AI team at Uber partnering with product and platform teams across the company to deliver cutting-edge machine learning solutions for core business problems. The Computer Vision team in AppliedAI specializes in Generative AI, Foundation Model, and classical Computer Vision solutions, and the ML infrastructure needed to scale these systems in production.
• Fabric Controller brings together platforms that keep large scale systems resilient and easy to operate, including Cadence and Repair Engine. Cadence is an open source workflow orchestration engine that manages distributed state, retries, scaling, and recovery so teams can focus on business logic while supporting mission critical workloads across many industries. Repair Engine detects, diagnoses, and remediates issues by correlating signals across infrastructure and application layers through an ingestion pipeline and statistical analysis. An ML engine is being developed to improve diagnostics by learning patterns from historic issues, reducing false positives, and incorporating broader context such as workload types and traffic trends. Together these systems strengthen reliability, automation, and operational efficiency across the stack.

• Drive exciting, ambitious, previously unsolved projects from end to end
• Develop novel algorithms that use Uber data at global scale
• Thrive in an environment with ambiguous product requirements
• Collaborate closely with product managers and data scientists
• Be motivated to independently own and drive projects forward
• Have a passion to make Uber better for our customers

• Currently enrolled in a Ph.D. program studying computer science, machine learning, data mining, artificial intelligence, constrained optimization, statistics, or a related quantitative field
• Candidates must have at least one semester/quarter of their education left following the internship
• Knowledge of underlying technical foundations of statistics, machine learning, optimization, systems, etc.
• Experience in one or more object-oriented languages, including C++, Java, Python, or Go

• Ability to communicate effectively with both technical and business partners
• Experience in simplifying/converting business problems into technical problems
• Research mentality with a bias towards action to structure a project from idea to experimentation to prototype to implementation