Maplebear Inc.

Aoshi Li

Ajay Pankaj Sampat

Mengyu Zhang

Aman Jain

Jaclyn Tandler

Youdan Xu

Zhongqiang Liang

Brendan Evans Ashby

Rucheng Xiao

Roman Havran

Francois Campbell

Pak Hong Wong

Shuo Feng

Kevin Green

Ziwei Shi

Leo Kwan

Chia-Eng Chang

Emily Silberstein

Van Nguyen

An online concierge system allows customers to place orders to be fulfilled by pickers. An order includes an amount of compensation a customer provides to a picker when the order is fulfilled. A customer may modify the amount of compensation provided to a picker, so some customers may initially specify a large amount of compensation to entice a picker to fulfill an order and then reduce the amount of compensation when the order is fulfilled. To prevent penalizing pickers who fulfilled an order without a problem, the online concierge system trains a model to determine a probability that a reduction in compensation to a picker was unrelated to a problem with order fulfillment. The online concierge system may perform one or more remedial actions for a picker based on the probability determined by the model.

Detection and remediation of improper value modification using machine learning

A system validates code ownership of software components identified in a build process. The system receives a pull request identifying a set of software components. The system analyzes code ownership of each software component using machine learning. The system provides features describing the software components as input to a machine learning model. The system determines based on the output of the machine learning model, whether the code ownership of the software component can be determined accurately. If the system determines that a software component identified by the pull request cannot be determined with high accuracy, the system may block the pull request or send a message indicating that the code ownership of a software component cannot be determined accurately.

Validating code ownership of software components in a software development system

An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

Using a predictive model to identify context features causing above-average tip amount

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Toronto, Canada

Aoshi Li