About the role:
Collaborates 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 mission of the Incentive Optimization team is to build machine learning models to optimize driver incentive spend, improving Uber's path toward profitability.
You will work with other engineers, data scientists, and product managers to deliver models and features that improve incentive spend efficiency and drive business growth. We are a data driven team, and you will be able to see the impact of your work reflected in Uber's earning report including revenue and profits.
Minimum qualifications:

• Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics or related field, WHICH INCLUDES 1-year 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
• Machine learning (e.g., tree-based techniques, supervised learning)
• Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib

• Note the 1-year 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 year of specialized experience is not necessarily in addition to the years of Education & full-time work experience indicated.

Technical skills:
Required:

• Feature management