Scientist Intern, Uber Eats Feed/Discovery

Job Description

We’re seeking highly motivated and skilled Ph.D. candidates for a Scientist Intern position within the Uber Eats Feed/Discovery team during the Summer 2026 (12-week program). This is an unparalleled opportunity to immerse yourself in a dynamic product team, tackling real-world Uber challenges under the guidance of an experienced Scientist. You will engage in collaborative partnerships with Scientists, Software Engineers, Product Managers, and various cross-functional stakeholders, making a tangible impact on millions of users globally.

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### About The Role

At Uber, we believe in making everyday “magic” happen. Have you ever pondered the intricate logistics behind your Uber ride arriving so quickly, or how Uber Eats seamlessly connects you with your favorite meals, predicting driver movements and ensuring timely deliveries? Have you wondered how the Uber Eats Feed generates hyper-personalized and relevant recommendations tailored to your unique tastes and context? If these questions pique your curiosity, then Uber’s Sciences division is the place for you.

Our mission is to infuse intelligence into Uber’s vast marketplace. This involves navigating complex trade-offs, ingeniously blending advanced algorithms with human resourcefulness, and distilling simplicity from inherent complexity. Achieving this delicate balance across our multi-sided platform is what creates the “Uber magic.” We specialize in building sophisticated systems that can predict future market dynamics, crafting the most cost-efficient marketplace for matching supply with demand. Our passion lies in leveraging innovative economics, machine learning, and scalable distributed software to automate and optimize every facet of this intricate dance between marketplace participants.

As a Scientist Intern, you will be deeply involved in every stage of the product development lifecycle. You will utilize data-driven insights to inform critical product decisions, build powerful predictive and prescriptive models that underpin our solutions, and even contribute to the development of platform tools used extensively across teams, with a primary focus on Mobility and Delivery. Join us in enabling millions of earners worldwide to deliver this seamless experience!

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### About The Team

The Uber Eats Feed Team is at the forefront of shaping the user experience. We are looking for a highly skilled and motivated Scientist to join this pivotal team. In this role, you will be instrumental in enhancing the discovery, ranking, and overall experience within the Home and Category Feeds for millions of Uber Eats users globally.

You will apply your profound expertise in data analysis, machine learning, and statistical modeling to extract critical insights and develop cutting-edge algorithms. Your contributions will directly lead to improvements in user satisfaction and operational efficiency. This isn’t merely about “tuning models”; you will partner closely with Machine Learning Engineers (MLEs) to architect the next generation of Recommender Systems (RecSys). You will lead the scientific design of models that optimize the entire user experience, from discovery to fulfillment. Crucially, you will also design and implement robust experimentation frameworks to rigorously validate the impact of your solutions on our complex, multi-sided ecosystem, ensuring both technical rigor and significant business impact.

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### What You’ll Do

As a Scientist Intern on the Uber Eats Feed Team, you will:

• End-to-End Project Ownership: Collaborate closely with a dedicated mentor to define a high-impact business problem, scope a compelling project, and then develop and prototype innovative solutions using rigorous data-driven approaches.
• Algorithmic Innovation: Design and implement sophisticated Machine Learning algorithms and objective functions that effectively unify competing business interests, such as organic relevance and sponsored content, into a cohesive value space. This involves intricate trade-off analysis and optimization.
• Productionize Solutions: Work in tandem with Software Engineers and Product Managers to translate your research prototypes into scalable, production-ready solutions that can operate at Uber’s immense scale.
• Strategic Influence: Prepare and present your research findings and recommendations to senior leadership, directly informing and influencing critical product and business decisions.
• Elevate Scientific Standards: Contribute to establishing and propagating best practices for scientific rigor across the team, covering areas such as advanced modeling, efficient coding, robust analytics, comprehensive optimization, and meticulous experimentation design.
• Impactful Experimentation: Design and conduct A/B tests and other experimental methodologies to scientifically validate hypotheses, measure the impact of new features, and drive data-backed business decisions.

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### Basic Qualifications

• Currently pursuing a Ph.D. in a highly quantitative field such as Economics, Operations Research, Statistics, Machine Learning, Computer Science, Applied Mathematics, or other closely related disciplines.
• Must have at least one semester/quarter of academic coursework remaining after the completion of the Summer 2026 internship.
• Demonstrably strong problem-solving abilities coupled with exceptional analytical thinking skills.

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### Preferred Qualifications

• Advanced Programming Skills: Proficiency in programming languages commonly used for data science and machine learning, including Python, R, and SQL. Experience with distributed computing frameworks like Spark is a significant plus. Familiarity with A/B Testing platforms and methodologies.
• Data Visualization Expertise: Experience with data visualization tools and libraries, including open-source options (e.g., Matplotlib, Seaborn, ggplot2) or commercial platforms (e.g., Tableau, Mixpanel, Looker, or similar).
• Deep Mathematical Foundations: A robust understanding of the underlying mathematical and statistical principles in statistics, machine learning, optimization, stochastic processes, economics, and advanced analytics.
• Relevant Domain Experience: Practical experience in areas such as Exploratory Data Analysis, Statistical Analysis, Model Development, Operations Management, Revenue Management and Pricing, Advertising, Experimental Design, Assortment Planning, or Transportation logistics.
• Professional Analytical Experience: 0-2 years of prior work experience in an analytical, research, or data science setting.
• Organizational Prowess: Highly organized, detail-oriented, and capable of working independently on multiple concurrent projects, managing time and priorities effectively.
• Specialized Domain Knowledge: Expertise in Ranking algorithms, Recommender Systems (RecSys), or Search technologies.
• Advanced Modeling Techniques: Experience with sophisticated modeling approaches such as Reinforcement Learning, multi-task learning, or auto-regressive models.
• Exceptional Communication: Proven ability to communicate complex technical concepts and business implications effectively to both highly technical audiences and non-technical business partners.
• Growth Mindset: Open to constructive feedback, with a strong aptitude for rapidly implementing newly learned ideas and concepts.
• Action-Oriented Research: Possesses a strong research mentality, coupled with a practical bias towards action. This includes the ability to structure a project comprehensively, from initial ideation through experimentation, prototyping, and ultimately to full implementation.
• Proactive and Independent: Demonstrates a self-starter mindset, excellent communication skills, and outstanding follow-through. You are enthusiastic about taking ownership of your work and thrive on the responsibility of being individually empowered to drive impact.

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### Compensation

• For San Francisco, CA-based roles: The base hourly rate for this role is USD$67.00 per hour.
• For Sunnyvale, CA-based roles: The base hourly rate for this role is USD$67.00 per hour.
• For all US locations, you will also be eligible for various benefits.