Senior Software Engineer – ADAS

Senior Software Engineer – ADAS page is loaded## Senior Software Engineer – ADASlocations:
Germany, Munich time type:
Full time posted on:
Posted Todayjob requisition :
JR2013471

Step into the future with NVIDIA, a global leader in AI computing, data science, and graphics, driving innovation in Artificial Intelligence, Deep Learning, and Autonomous Vehicles. Our team of visionaries is reshaping industries worldwide with cutting-edge technologies. Join We’re hiring a mid‐level Software Engineer to develop production ADAS and autonomous driving functions in C++ and Python. If you’re passionate about building robust, high‐performance features that run on GPUs in real vehicles, we’d like to hear from you.
** What you’ll be doing:
*** Design, implement, and maintain   C++ ADAS functions   for perception, prediction, and planning (e.g., lane keeping, ACC, AEB, traffic‐light and object handling) in a safety‐critical codebase.
* Integrate deep learning models into C++ pipelines: take models trained in Python (PyTorch or Tensor Flow), export/convert them, and deploy them for real‐time inference on NVIDIA GPUs.
* Work with multi‐sensor data — cameras, radar, lidar — and implement sensor fusion, tracking, and decision‐making logic in C++.
* Build and extend testable, modular libraries and components, including interfaces to models, sensor drivers, and vehicle control.
* Profile, debug, and optimize C++ and CUDA code to meet strict latency and throughput targets.
* Contribute to tooling around data quality, automated evaluation, and regression tests for ADAS functions.
* Collaborate closely with ML researchers, systems engineers, and automotive partners to turn prototype algorithms into production‐ready C++ implementations.
** What we need to see:
*** 4–8 years of professional software engineering experience, ideally in   ADAS, automotive, robotics, or real‐time systems  .
* Master’s or PhD degree in Computer Science or in Machine Learning
* Strong modern C++ (C++14/17 or later): templates, RAII, smart pointers, STL, and experience building large codebases.
* Solid Python skills for tooling, training scripts, and glue code between data pipelines and C++ components.
* Hands‐on experience training and using deep learning models (PyTorch or Tensor Flow): designing experiments, tuning hyperparameters, working with large datasets, and debugging model behavior.
* Experience developing on Linux: build systems (CMake), debugging (gdb, sanitizers), profiling, and git‐based workflows in a CI/CD environment.
* Familiarity with one or more of:
- GPU programming and optimization (CUDA, Tensor

RT, cuDNN)    - Computer vision / perception (object detection, segmentation, multi‐object tracking)    - Robotics or autonomous systems (ROS/ROS2, ADAS features, simulation environments)
** Ways to stand out from the crowd:
*** Direct experience implementing   ADAS functions in C++  , such as lane keeping, adaptive cruise control, automatic emergency braking, or traffic‐sign/traffic‐light handling.
* Experience with camera calibration, sensor fusion, or multi‐camera perception systems.
* Knowledge of model optimization and deployment: quantization (INT8, FP8, 4‐bit), Tensor

RT‐LLM, ONNX Runtime, or similar frameworks.
* Background in training infrastructure: distributed training, experiment tracking, dataset versioning, hyperparameter optimization.
* Understanding of software quality practices for safety‐critical systems (code review, unit testing, static analysis; automotive standards knowledge is a plus) as well as open‐source contributions or published work in AI, robotics, or GPU computing.

Work on challenging, real‐world ADAS and autonomous driving problems where your C++ and ML skills directly impact vehicle safety and performance. Collaborate with a talented, multidisciplinary team of researchers, engineers, and automotive experts. Solve hard technical problems at the intersection of deep learning, real‐time systems, and production software engineering. If this opportunity aligns with your background and interests, please apply with your resume and a brief description of relevant ADAS or autonomy projects (links to Git Hub, publications, or technical write‐ups are welcome)
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