Senior Software Engineer – AI and Autonomous Driving

NVIDIA NVIDIA · Semiconductors · Munich, Germany

Senior Software Engineer to build and deploy production AI for autonomous vehicles, focusing on training, fine-tuning, and optimizing deep learning models for real-time inference on NVIDIA GPUs. Requires strong C++/Python, deep learning training experience, and Linux development skills, with a preference for GPU programming, computer vision, or robotics.

What you'd actually do

  1. Design, develop, and maintain C++ and Python software for perception, prediction, and planning in advanced driver‑assistance and autonomous driving systems.
  2. Train, fine‑tune, and iterate on deep learning models (vision, multimodal, and transformer‑based architectures) using large‑scale driving datasets, then optimize them for real‑time inference on NVIDIA GPUs.
  3. Work with multi‑sensor data — cameras, radar, lidar — and contribute to training pipelines, data quality workflows, and automated evaluation infrastructure.
  4. Debug and resolve performance bottlenecks, edge cases, and integration challenges in a complex, safety‑critical codebase.
  5. Collaborate with ML researchers, systems engineers, and automotive partners to bring features from research prototypes to production‑ready systems.

Skills

Required

  • 4–8 years of professional software engineering experience
  • Proficiency in C++ (modern C++14/17 or later) and Python
  • demonstrated experience writing clean, maintainable code
  • Hands-on experience training deep learning models (PyTorch or TensorFlow)
  • designing experiments
  • tuning hyperparameters
  • working with large datasets
  • debugging model behavior
  • Strong Linux development skills
  • building
  • debugging
  • profiling
  • version control (git)
  • working within CI/CD workflows

Nice to have

  • GPU programming and optimization (CUDA, TensorRT, cuDNN)
  • Computer vision and perception (object detection, segmentation, multi‑object tracking)
  • Robotics or autonomous systems (ROS, ADAS features, simulation environments)
  • Experience with camera calibration, sensor fusion, or multi‑camera perception systems.
  • Knowledge of model optimization and deployment: quantization (INT8, FP8, 4‑bit), TensorRT‑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).
  • Open‑source contributions or published work in AI, robotics, or GPU computing.

What the JD emphasized

  • training deep learning models
  • optimize them for real-time inference

Other signals

  • production AI for autonomous vehicles
  • deploying robust, high-performance models that run on GPUs in real cars
  • train, fine-tune, and iterate on deep learning models
  • optimize them for real-time inference on NVIDIA GPUs