Senior Machine Learning Engineer - Automotive

NVIDIA NVIDIA · Semiconductors · Austria · Remote

Senior Machine Learning Engineer at NVIDIA, focusing on the automotive perception stack for self-driving cars. The role involves designing, training, and optimizing ML models for LiDAR/camera perception, developing end-to-end ML workflows, and productizing these models into the NVIDIA DRIVE AV platform using C++. Requires strong experience in computer vision, deep learning frameworks, and large-scale ML systems, with a focus on shipping production-ready AI for autonomous driving.

What you'd actually do

  1. Model Development: Design, train, and optimize innovative machine learning models for LiDAR/camera perception (e.g., object detection/classification, semantic segmentation, tracking).
  2. Develop and coordinate entire ML workflows, covering data pipelines, model training, model metrics, continuous performance instrumentation, and reporting.
  3. Productization: Take ML models and algorithms from initial evaluation and experimentation all the way to product level on the NVIDIA DRIVE AV platform, developing highly efficient product code in C++.
  4. Innovation: Keep track of the latest developments in machine learning, and incorporate techniques that improve platform performance.
  5. Collaborate with LiDAR/camera teams, developers, engineers, and managers to turn complex ideas into reliable solutions for autonomous driving.

Skills

Required

  • MS in Computer Science, Engineering, or a related field, or equivalent experience.
  • 6+ years of relevant proven industry experience applying machine learning to address real-world problems.
  • Strong C++ and Python programming and debugging skills with experience in developing for large, complex systems.
  • Deep practical experience applying machine learning to lidar/camera perception in automotive or related fields.
  • Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and a strong understanding of the mathematical foundations of ML.
  • Building and sustaining training and essential metric workflows for large-scale datasets.
  • Excellent communication and analytical skills.
  • Self-motivated drive to solve hard problems.

Nice to have

  • Proven track record of developing and shipping deep learning models for LiDAR/Camera in a production environment.
  • Familiarity with modern network architectures like Transformers and their application to visual recognition tasks.
  • A history of delivering ML features and models into a production autonomous vehicle stack or a related robotics product.
  • Experience with model optimization for real-time inference on embedded or automotive platforms (e.g., using TensorRT).

What the JD emphasized

  • Proven track record of developing and shipping deep learning models for LiDAR/Camera in a production environment.
  • Experience with model optimization for real-time inference on embedded or automotive platforms

Other signals

  • shipping ML models
  • automotive perception
  • real-time inference