Graduate 2025 PhD Software Engineer II (Machine Learning Platform), United States

**About the Role** At Uber, engineers collaborate with stakeholders to design, develop, optimize, and productionize machine learning (ML) or ML-based solutions and systems that are used within a team to solve moderately complex problems. This role also leverages and improves ML infrastructure for model development, training, deployment needs and scaling ML systems. **About the Team** The Machine Learning Platform is responsible for building the ML ecosystem and providing the tool chains for ML engineers at Uber. All critical ML applications at Uber are built and powered by this platform. The organization is formed of talented teams of engineers and ML engineers with a combination of both strong industrial and academia backgrounds. In particular, we are looking for Ph.D graduates to conduct research and bring in methodologies on some of following topics: artificial intelligence (NLP/LLM, embeddings, computer vision, recommendation systems, information retrieval), distributed algorithms and systems, and high performance computing (HPC). **What You’ll Do** - Drive exciting, ambitious, previously-unsolved projects from end to end - Thrive in ambiguous product requirements - Collaborate with product managers and machine learning engineers closely - Make data driven decisions, with exceptional execution - Be motivated to own projects and push them forward with independence - Most meaningfully, have a passion to make Uber better for our customers **Basic Qualifications** - Completing or recently completed a Ph.D. program in one of the following domains: model analytics, scientific computing, algorithms, operating systems, distributed systems, and/or a related field - Experience in Python - Experience with some ML frameworks, such as PyTorch, TensorFlow, JAX, sci-kit, or xgBoost **Preferred Qualifications** - Strong communication skills demonstrated with cross-functional partners, such as open source contributions, teaching experience or internships, presenting at industry recognized academic conferences - Proficiency in one or more coding languages such as Python, Java, Go, or C++ - Internship experience building and productionizing machine Learning systems - Experience in simplifying/converting business problems into technical problems or a good publishing record - Research mentality with a bias towards action to structure a project from idea to experimentation to prototype to implementation For San Francisco, CA-based roles: The base salary range for this role is USD$167,000 per year - USD$185,500 per year. For Seattle, WA-based roles: The base salary range for this role is USD$167,000 per year - USD$185,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$167,000 per year - USD$185,500 per year. For 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. You will also be eligible for various benefits. More details can be found at the following link [https://www.uber.com/careers/benefits](https://www.uber.com/careers/benefits). Uber is proud to be an Equal Opportunity/Affirmative Action 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). Offices 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.