Regular Python Developer

Luxoft is one of the major software services companies world-wide. In particular, we develop high quality software in automotive industry for most famous car makers.

The software inside a vehicle was traditionally expected to be a very controlled and self-contained environment. Equipping cars with perception and machine intelligence changes a lot in the overall picture of the vehicle manufacturer. Luxoft's goal is to empower our customers, with deep domain knowledge and smart solutions, to develop cars of the future.

The project is dedicated for development of the tools for virtual testing of ADAS systems using data-driven development approach. Tools to be extended to the 2025 kit and allow to test and validate higher automation level cars and bring them to the roads. The target automation level is 3, which means that the car will be fully responsible for driving under specific conditions (highways, specific areas, traffic jams, etc). To bring fully autonomous car to the roads and to bring the future to reality, its safety has to be proven, for this purpose the testing on private grounds or on public roads is not enough as it requires hundreds of millions of kilometers to ride, so the alternative way of testing - virtual testing and data based testing become more and more actual.

For the development of the next generation tooling for ADAS (Advanced Driver Assistance Systems) and HAD (Highly Automated Driving) functions for a major German car maker, we are looking for talented engineers and developers.

Join our enthusiastic and experienced teams and develop software for the vehicles of future that will be used on a daily basis by millions of drivers once the next generation of cars hits the roads starting from 2025.

Responsibilities:

Software Development, Participating in scrum events, Product Demonstration to the customers.

Development and operation of the framework for generating reference data for labelling algorithms in the form of KPIs (lane markings, obstacles, the lane area travelled over and the trajectory travelled). These must be adapted to both to local and the cloud infrastructure. The goal is to increase the accuracy in order to minimize the effort for manual corrections.