Senior Machine Learning Engineer - Automotive
Indexed description
Our team is building the machine learning backbone of the Perception component for NVIDIA DRIVE AV. We are seeking the best Machine Learning Engineers with a background in computer vision, LiDAR & camera perception, and AI infrastructure who are passionate about solving the hardest problems for self-driving cars. Are you interested in inventing human-level AI for perception under real world conditions? If so, join us!
What You'll Be Doing
- Model Development: Design, train, and optimize innovative machine learning models for LiDAR/camera perception (e.g., object detection/classification, semantic segmentation, tracking).
- Develop and coordinate entire ML workflows, covering data pipelines, model training, model metrics, continuous performance instrumentation, and reporting.
- 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++.
- Innovation: Keep track of the latest developments in machine learning, and incorporate techniques that improve platform performance.
- Collaborate with LiDAR/camera teams, developers, engineers, and managers to turn complex ideas into reliable solutions for autonomous driving.
- 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.
- LiDAR or Camera Perception Experience: Proven track record of developing and shipping deep learning models for LiDAR/Camera 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).
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