About The Role
Delivery Marketplace is a central pillar to Uber's delivery products. As the central brain of the company, we are the decision makers that make moving from point A to point B possible for every order that Uber serves, from UberEats to new verticals such as Grocery. We handle all the logic from making the dispatch decisions, predicting how long a delivery might take, and estimating optimal pickup times for orders. We build products that directly impact Uber's top and bottom lines.
Machine Learning Engineers (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 include:
- 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 functions which balance 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, and 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, and 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 all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of compensation. You will also be eligible for various benefits.
Benefits Extracted with AI
- Bonus program
- Equity award
- Health insurance
- Paid vacation
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