We are currently looking for 2020 summer interns to join our Mountain View office to develop algorithms and pipelines for DiDi projects in intelligent driving technologies. The goal is to apply the state of the art AI algorithms on cars to make driving smarter and safer.
Onboard infrastructure team cares about everything happening in the vehicle, from low-level OS to high level applications. The team is in charge of the whole software architecture and provides common libraries (threadpool, IPC, trace, stats, health monitoring, data recording, logging, etc) for other teams to use. We work closely with all other teams to build high performance and low-latency self-driving solution.
Develop and deploy tools and pipeline in the area of self-driving on vehicles to improve the driving quality and make it smarter and safer.
Work on the high performance software infrastructure of the self-driving system.
Design, implement, and employ components for common libraries used by all other teams.
Work on performance monitoring / optimization for modules running on Linux-based OS.
Collaborate cross functionally with hardware team to enable the use of new devices on the self-driving vehicles.
Pursuing Master degree or higher in Computer Science or related disciplines.
A solid foundation of data structures and algorithms.
Team player with good communication skills.
Attentive to detail and strive for engineering excellence.
Solid programming and proficient in C/C++.
Linux multithreaded / multi-process programming.
Familiarity with good engineering practices such as continuous integration, automated testing and code reviews.
About DiDi Labs
DiDi is a ride-sharing platform dedicated to revolutionizing the way people live and move.
Didi Chuxing offers a full range of on-demand mobility options, including Taxi hailing, private car hailing, Hitch (social ride-sharing), Chauffeur (designated driver), Bus, Minibus, Car Rental, and Enterprise Solutions.
Our company is committed to working with communities and partners to solve the world’s transportation and environmental challenges using big data-driven deep-learning algorithms that optimize resource allocation.