About the role:
Partners 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 complex problems with multiple dependencies. This role also leads team efforts to leverage and improve ML infrastructure for model development, training, deployment needs and scaling ML systems.
About the Team:
Whether engineering a more efficient query understanding or providing riders personalized ranking results, our search technologies are integral to the magic of the Uber platform. On the Maps Engineering team, we use the latest ML, NLP, and neural network solutions to help users find the intended locations online more intelligently and efficiently.
We are a very small team of engineers responsible for determining a convenient origin and destination of all trips worldwide. We own the search platform and backend services that power the pickup and dropoff experiences for all Uber jobs - be it Rides, Eats, or Freight! As a machine learning engineer on this team you would help us build out the ML models that drive everything from ETA calculations to determining the optimal pickup and drop off for riders and couriers globally. You will be working with some of the world's most experienced mapping professionals, data scientists, software engineers, and research scientists on a user-facing products with global impact. This is your chance to develop cutting-edge technology that will touch every Uber trip!
Minimum 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.

Technical skills:
Required:

• Scalable ML architecture
• Feature management

Preferred:

• Deep Learning
• Deep Learning
• Experience in search or information retrieval area, natural language processing
• Experience in personalization models
• At least five (5) years of software engineering experience and building production scale ML models
• Experience shipping high-quality features on schedule
• Experience building large scale distributed systems
• Experience implementing projects with multiple dependencies
• Detailed problem-solving approach and knowledge of algorithms, data structures, and complexity analysis