About the Role

Uber’s Applied AI team is at the forefront of leveraging cutting-edge artificial intelligence and machine learning technologies to enhance the Uber experience for millions of users globally. The Applied AI team collaborates with product teams across Uber to deliver innovative AI solutions for core business problems. We work closely with engineering, product and data science teams to understand core business problems and the potential for AI solutions, then deliver those AI solutions end-to-end.

The Applied AI team is building Natural Language and Computer Vision capabilities for multimodal document understanding. We are looking for a strong Machine Learning Engineer to join our team in Seattle!

What You’ll Do

• Train, evaluate and deploy Machine Learning models.
• Develop distributed pipelines to analyze and process large datasets.
• Collaborate with cross-functional teams to understand business problems and deliver an end-to-end Machine Learning solution.
• Stay current with the latest advancements in AI/ML and integrate new technologies into the team’s work.

Basic Qualifications

• PhD or equivalent in Computer Science, Engineering, Mathematics or related field OR 3-years full-time Software Engineering work experience, WHICH INCLUDES 2-years total technical software engineering experience in one or more of the following areas:
• Programming language (e.g. C, C++, Java, Python, or Go)
• Training using data structures and algorithms
• Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning)
• Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib
• Note the 2-years total of specialized software engineering experience may have been gained through education and full-time work experience, additional training, coursework, research, or similar (OR some combination of these).  The years of specialized experience are not necessarily in addition to the years of Education & full-time work experience indicated.
• Experience in programming with Python.
• Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
• Experience in the development, training, productionization and monitoring of ML solutions at scale.

Preferred Qualifications

• 4 years of Software Engineering work experience.
• Experience in modern deep learning architectures and probabilistic models.
• Experience in Natural Language Processing (NLP) and Computer Vision.
• Experience in modern generative AI, such as transformer architectures, diffusion models and prompting.
• Publications or contributions to AI/ML research in top conferences or journals.

For San Francisco, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 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.

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.

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.