Wenhan Shen and Lev Neiman    Financial partnerships are tricky to manage which is why DoorDash needed the right technology stack to be able to quickly onboard new Dashpass partners The challenge was that each partner brought with them a diverse set of conditions and rules that our system needed to be able to accommodate

Ryan Sokol    On the evening of Thursday May between the hours of PDT and PDT DoorDash experienced a system wide failure for approximately three hours During that period many Dashers were unable to accept new deliveries consumers had trouble ordering from our mobile app merchants prepared

Sonic Wang, Xiaochang Miao and Yongshuang Wang    Focusing on delivery allowed DoorDash to build a food search engine but expanding beyond food with more SKUs and merchants requires a substantial upgrade As DoorDash expands into new verticals such as groceries convenience alcohol and pet food new challenges arise that our food driven legacy

Kornel Csernai    At DoorDash we make millions of predictions every second to power machine learning applications to enhance our search recommendation logistics and fraud areas and scaling these complex systems along with our feature store is continually a challenge To make predictions we use a microservice

Christopher Meiklejohn    Three key steps are of paramount importance to prevent outages in microservice applications especially those that depend on cloud services Identify the potential causes for system failure prepare for them and test countermeasures before failure occurs Because DoorDash s complex infrastructure

Lu Wang, Ying Yang and Chen Dong    An optimized merchant selection strategy has been one of the key factors that has enabled DoorDash to become an industry leader in US food delivery service DoorDash was founded later than many companies in this industry but we have effectively onboarded high value merchants to ensure the

DoorDash    Liangxiao is a powerhouse of knowledge skills perseverance and empathy Her extensive technical experience coupled with her ability to distill complex problems into simple and straightforward solutions is nothing short of extraordinary Based in the Seattle office Liangxiao came to DoorDash

Hien Luu    Taking full advantage of a large and diverse set of machine learning ML use cases calls for creating a centralized platform that can support new business initiatives improve user experiences enhance operational efficiency and accelerate overall ML adoption For a hypergrowth company like

Pratik Parekh and Zhe Jia    For DoorDash being able to predict long tail events related to delivery times is critical to ensuring consumers orders arrive when expected Long tail events are rare instances where time predictions are far off from actual times and these events tend to be costly While we previously improved

Sri Venkatesh Sankaran    To safeguard our users accounts and prevent fraud we sometimes ask users to verify their identity or confirm a transaction by completing a user friction such as two factor authentication User frictions or verification steps designed to prevent fraud are essential in combating fraudulent