We are looking for experienced software engineers to join our Mountain View office to develop algorithms and pipelines for DiDi projects in intelligent driving technologies. You will be responsible for developing state of the art AI algorithms, as well as building highly reliable software and large-scale distributed systems. Your work will be applied on real vehicles to be deployed in DiDi’s network.
Develop and deploy tools and pipeline in the area of intelligent driving on vehicles to improve the driving quality and make it smarter and safer.
Research and implement the state of the art algorithms, adapt them into driving scenarios and make it running real-time on cars.
Collaborate with third-party car companies and other groups to deliver the intelligent vehicles.
Contribute to Company’s intellectual property through patent filing.
Bachelor or master degree in Computer Science or related disciplines.
Solid programming and proficient in C++ and/or Python.
Low-latency, real-time software; performance analysis and optimization.
Linux multithreaded / multi-process programming.
Familiarity with good engineering practices such as continuous integration, automated testing and code reviews.
3+ years of industrial working experience.
Clear in communication and easy to collaborate in a growing team.
Master or Ph.D. degree in area of Computer Science, Electrical and Computer Engineering or a related field with a focus in machine learning, artificial intelligence, optimization, operating systems, large-scale distributed systems.
Qt, GPU, OpenGL programming.
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.