Senior Machine Learning Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior Machine Learning Engineer at NVIDIA, focusing on the Perception component for NVIDIA DRIVE AV. The role involves designing, training, and optimizing ML models for LiDAR/camera perception and multi-sensor fusion, developing entire ML workflows, and productizing ML models into C++ code for the NVIDIA DRIVE AV platform. Requires strong C++/Python skills, deep ML experience in automotive perception, and familiarity with deep learning frameworks. Experience with Transformers, AV production, and inference optimization is a plus.

What you'd actually do

  1. Model Development: Design, train, and optimize innovative machine learning models for LiDAR/camera perception and multi-sensor fusion (e.g., object detection/classification, image 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 or PhD in Computer Science, Engineering, or a related field, or equivalent experience
  • 8+ 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 and multi-sensor fusion 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

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

What the JD emphasized

  • proven industry experience applying machine learning to address real-world problems
  • Deep practical experience applying machine learning to lidar/camera perception and multi-sensor fusion in automotive or related fields
  • Building and sustaining training and essential metric workflows for large-scale datasets
  • Proven track record of developing and shipping deep learning models for LiDAR/Camera and Multi-Sensor Fusion in a production environment
  • Experience with model optimization for real-time inference on embedded or automotive platforms

Other signals

  • shipping ML models
  • production ML
  • autonomous driving