Sr Staff Engineer, AI and ML Platforms

About the Role

Leads efforts within the organization to drive the innovation, development and maintenance of UberAI’s machine learning efforts.

Uber is on the lookout for top-notch software engineers to join and drive innovation on Machine Learning Platform (Michelangelo) team. This role involves building and managing robust distributed systems, and solve infrastructure challenges to empower Uber’s product engineering and data science teams with latest technologies in large scale Artificial Intelligence.

The Michelangelo team works on building end to end ML systems and up-leveling ML quality at Uber. You will be part of a team of strong software and systems engineers, executing in a fast paced environment. For more information on the Michelangelo Machine Learning Platform, see our select blog posts:

• Meet Michelangelo: Uber’s Machine Learning Platform
• Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber
• Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development
• Continuous Integration and Deployment for Machine Learning Online Serving and Models
• Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow
• Elastic Distributed Training with XGBoost on Ray

What You’ll Do

This role involves building and managing robust distributed systems, and solve infrastructure challenges to empower Uber’s product engineering and data science teams with latest technologies in large scale Artificial Intelligence.

Minimum Qualifications

PhD or equivalent in Computer Science, Engineering, Mathematics or related field AND 6-years full-time Software Engineering work experience OR 8-years full-time Software Engineering work experience, WHICH INCLUDES 6-years total technical software engineering experience in one or more of the following areas:

• Programming language (e.g. C, C++, Java, Python, or Go)

Note the 6-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.

Required:

• Scalable ML Infra Knowledge
• Experience in building and managing distributed systems and high-throughput services.
• Systematic problem solving approach and knowledge of algorithms, data structures and complexity analysis.
• Experienced production user of Deep Learning frameworks such as Apache SparkML, XGBoost, Ray, Tensorflow, PyTorch, Keras, etc.

Preferred:

• Experience in managing dependencies in data science packages.
• Experience in high performance computing.
• Grit, drive and a strong feeling of ownership coupled with collaboration and leadership.

For Sunnyvale, CA-based roles: The base salary range for this role is $243,000 per year - $270,000 per year.

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.