Uber Machine Learning Engineer Delivery Matching 

United States, West Virginia 

12884027

About the Role

MLEs lead efforts within the team and broader Delivery Marketplace organization to drive ideation, development and productionization of optimization solutions with real-time and ML-based signals that solve strategically important problems. Some existing problem spaces that the team works on:

• Using statistical/machine learning/forecasting models for demand and supply models
• State of the art prediction models for estimating food preparation times, batching quality as well as time spent by couriers at restaurants picking up items.
• Develop objective function which balances magical user experience and economics of the business

It is a challenging yet rewarding job. You will have a lot of opportunities to work with product managers, data scientists and engineers from other teams. You will be in-charge of solving Uber scale problems with the right techniques like reinforcement learning/deep learning/optimization methods.

What the Candidate Will Do

• Drive the design, development, optimization, and productization of machine learning (ML) solutions and systems that are used to solve strategically important or vaguely defined problems.
• Build ML solutions to improve Delivery marketplace efficiency while delivering magical user experience

Basic Qualifications

• PhD or equivalent experience in Computer Science, Engineering, Mathematics or a related field and 2 years of Software Engineering work experience.
• Experience in programming with a language such as Python, C, C++, Java, or Go.
• Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
• Experience with SQL and database systems such as Hive, Kafka, and Cassandra.
• Experience in the development, training, productionisation and monitoring of ML solutions at scale.

Preferred Qualifications

• Experience in modern deep learning architectures and probabilistic models.
• Experience in optimization (RL / Bayes / Bandits) and online learning.
• Experience in causal inference/personalization/ranking

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.