Senior Staff Machine Learning Engineer - Delivery Matching at Uber

## About the RoleDelivery 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.Senior Staff MLE 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 businessIt 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 guide/mentor a group of MLEs in the end-to-end development cycle from product ideation, model development and  productionisation at scale
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- Lead 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- Lead ML engineers, provide technical leadership and vision for the team.Basic Qualifications- PhD or equivalent experience in Computer Science, Engineering, Mathematics or a related field and 8 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 a technical leadership role and mentoring junior engineers.- Experience in modern deep learning architectures and probabilistic models.- Experience in optimization (RL / Bayes / Bandits) and online learning.- Experience in causal inference/personalization/rankingFor New York, NY-based roles: The base salary range for this role is USD$252,000 per year - USD$280,000 per year.For San Francisco, CA-based roles: The base salary range for this role is USD$252,000 per year - USD$280,000 per year.For Sunnyvale, CA-based roles: The base salary range for this role is USD$252,000 per year - USD$280,000 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 comp
You will also be eligible for various benefits
More details can be found at the following link [ 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
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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.