{"id":"u8a624o505","title":"Graduate 2026 PhD Software Engineer II (Consumer Structural Pricing), United States","posted_at":"2026-02-09T16:00:00.000Z","apply_url":"https://www.uber.com/us/en/careers/list/155342","locations":["San Francisco, CA"],"employment_type":null,"workplace_type":null,"seniority_level":null,"description_language":"en","source_name":"uber_sites","source_url":"https://www.uber.com/us/en/careers/list/155342","salary":{"min":171000,"max":null,"currency":"USD","period":"year","display":"$171,000+"},"job_summary":null,"job_description":null,"description_text":"We’re looking for machine learning engineers who are currently completing or recently completed a PhD program and who are passionate about building high-impact, consumer-facing products. In this role, you'll work across the full end-to-end flow of Uber's Delivery products—from building machine learning models and offline data pipelines, to developing real-time services, online model serving, and product user experiences. You’ll have the opportunity to tackle challenging problems at scale, shape core pricing mechanisms, and drive impactful outcomes across the entire consumer journey.\n\n**About the Team**\n\nThe **Consumer Structural Pricing** team plays a pivotal role in shaping consumer demand across Uber’s Delivery business—including food delivery, groceries, and more. We work closely with other Uber Marketplace teams to build innovative products and scalable systems that keep the marketplace efficient, reliable, and ready for continued growth. Our systems power hundreds of millions of consumers and millions of merchants around the world, and that footprint is expanding rapidly.\n\n**What You’ll Do**\n\n- Design and build innovative products used by hundreds of millions of consumers, in collaboration with talented engineers, Product Managers, Product Operations, and Applied/Data Scientists\n- Develop and optimize ML models to enhance the efficiency of key delivery marketplace pricing levers\n- Build offline data pipelines using Hive or similar technologies\n- Write clean, maintainable, and high-quality code\n\n**Basic Qualifications**\n\n- Completing or recently completed a PhD in Statistics, Mathematics, Computer Science, Machine Learning, or related quantitative field\n\n**Preferred Qualifications**\n\n- Experience in designing and crafting scalable, reliable, maintainable and reusable ML solutions using deep-learning techniques and statistical methods\n- Strong sense of ownership and accountability\n- Strong problem-solving skills, with expertise in ML methodologies\n- Ability to thrive in a fast-paced, collaborative, and team-oriented environment\n\nFor San Francisco, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year.\n\nFor Sunnyvale, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year.\n\nFor all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link [https://jobs.uber.com/en/benefits](https://jobs.uber.com/en/benefits).\n\nUber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.\n\nUber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing [this form](https://forms.gle/aDWTk9k6xtMU25Y5A).\n\nOffices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.","visa_sponsorship":null,"experience_years_min":null,"job_address":null,"job_city":null,"job_state":null,"job_country":null,"location_lat":37.7879363,"location_lng":-122.4075201,"closed_at":null,"apply_with_ai_eligible":false,"keywords":["product managers","machine learning","data scientists","collaboration","quantitative","maintainable","high-quality","experiences","end-to-end","operations","statistics","real-time","efficient","pipelines","ML models","scalable","reusable","business","consumer","develop","design","Growth","teams","clean","forms","Legal","speed","data","ML"],"company":{"name":"Uber","slug":"uber","logo_url":"https://img.logo.dev/uber.com?token=pk_fWx5G5QrQMm-0Ud8BW3mBg&size=64&format=png","description":"Uber is a technology company that operates a global platform for ride-sharing and food delivery services, including its Uber Eats business.","website_url":"https://www.uber.com/","linkedin_url":"https://www.linkedin.com/company/uber-com","glassdoor_url":"https://www.glassdoor.com/Overview/Working-at-Uber-EI_IE575263.11,15.htm","x_url":"https://x.com/Uber","instagram_url":"https://www.instagram.com/uber/","youtube_url":"https://www.youtube.com/Uber","github_url":"https://github.com/uber","huggingface_url":null,"tiktok_url":"https://www.tiktok.com/@uber","crunchbase_url":"https://www.crunchbase.com/organization/uber","facebook_url":"https://www.facebook.com/uber/","employee_count_range":"10000+","employee_count":31100,"founded_year":2009,"headquarters":{"address":"1725 3rd Street, San Francisco, CA 94158, United States","city":"San Francisco, CA","country":"US","lat":37.7879363,"lng":-122.4075201},"industry":"logistics","company_type":"other","total_funding_usd":null,"locations":["Aarhus","Aarhus, Denmark","Accra, Ghana","Aguascalientes, Mexico","Amsterdam","Amsterdam, Netherlands","Athens","Athens, Greece","Atlanta, Fulton","Atlanta, GA","Auckland, New Zealand","Austin, TX","Barcelona, Spain","Bengaluru, India","Bengaluru, Karnātaka","Berlin","Berlin, Germany","Birmingham, United Kingdom","Bogota","Bogota, Colombia","Bordeaux, France","Boston, MA","Brasilia, Brazil","Bristol, United Kingdom","Brussels","Brussels, Belgium","Bucharest, Romania","Buenos Aires, Argentina","Cairo","Cairo, Egypt","Cancun","Celaya","Chicago, Cook","Chicago, IL","Ciudad Juarez","Cologne","Cologne, Germany","Colombo, Sri Lanka","Copenhagen, Denmark","Culiacan","Culiacan, Mexico","Dallas, TX","Denver, CO","Dhaka, Bangladesh","Dubai","Dubai, United Arab Emirates","Dublin","Dublin, Ireland","Guadalajara, Mexico","Guayaquil, Ecuador","Gurgaon, Haryāna","Gurgaon, India","Gurugram, Haryāna","Helsinki, Finland","Hermosillo","Hong Kong, Hong Kong","Honolulu, HI","Hyderabad, India","Hyderabad, Telangāna","Istanbul, Turkey","Johannesburg","Johannesburg, South Africa","Kaohsiung City","Kaohsiung City, Taiwan","Krakow","Krakow, Poland","Leeds, UK","Leeds, United Kingdom","Leon","Leon, Mexico","Lima","Limerick","Limerick, Ireland","Lisbon","Lisbon, Portugal","London, UK","London, United Kingdom","Los Angeles, CA","Lyon, France","Mabalacat","Mabalacat, Philippines","Madrid","Madrid, Spain","Manchester, UK","Manchester, United Kingdom","Marseille, France","Melbourne","Melbourne, Australia","Mendoza","Mexico City, Mexico","Miami, FL","Milan, Italy","Monterrey","Monterrey, Mexico","Montreal, Canada","Morelia","Morelia, Mexico","Mumbai, India","Munich","Munich, Germany","Nairobi, Kenya","Nashville, TN","New Taipei City, Taiwan","New York, NY","Oslo, Norway","Panama City","Panama, Panamá","Paris","Paris, France","Phoenix, AZ","Phoenix, Maricopa","Prague, Czech Republic","Puebla","Puebla, Mexico","Quito, Ecuador","Randburg","Randburg, South Africa","Rio de Janeiro, Brazil","Riyadh, Saudi Arabia","San Francisco, CA","San José, Costa Rica","San Luis Potosi, Mexico","San Rafael de Escazu, Costa Rica","Santiago","Santiago de Queretaro","Santiago de Queretaro, Mexico","Santiago, Chile","Santo Domingo, Dominican Republic","Seattle, WA","Seoul, Korea","Singapore","Stockholm","Stockholm, Sweden","Strasbourg, France","Sunnyvale, CA","Sunnyvale, Santa Clara","Sydney","Sydney, Australia","São Paulo, Brazil","Taguig City","Taguig City, Philippines","Taichung","Taichung, Taiwan","Taipei","Taipei, Taiwan","Tijuana","Tlalnepantla, Mexico","Tokyo","Tokyo, Japan","Toluca, México","Toronto, Canada","Toronto, Ontario","Uusimaa","Vienna","Vienna, Austria","Villahermosa, Mexico","Visakhapatnam, India","Warsaw","Warsaw, Poland","Washington, DC","Zurich","Zurich, Switzerland"]},"structured_data":{"@context":"https://schema.org","@type":"JobPosting","@id":"https://worklittle.com/jobs/u8a624o505#job","title":"Graduate 2026 PhD Software Engineer II (Consumer Structural Pricing), United States","description":"Graduate 2026 PhD Software Engineer II (Consumer Structural Pricing), United States at Uber. View role details and apply on Worklittle.","datePosted":"2026-02-09T16:00:00.000Z","validThrough":"2026-03-11T16:00:00.000Z","url":"https://worklittle.com/jobs/u8a624o505","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Uber","sameAs":"https://www.uber.com/","logo":"https://img.logo.dev/uber.com?token=pk_fWx5G5QrQMm-0Ud8BW3mBg&size=64&format=png"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","streetAddress":"1725 3rd Street","addressLocality":"San Francisco","addressRegion":"CA","postalCode":"94158","addressCountry":"US"}},"employmentType":"FULL_TIME","identifier":{"@type":"PropertyValue","name":"Uber","value":"u8a624o505"},"baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":171000,"maxValue":171000,"unitText":"YEAR"}}}}