Senior Machine Learning Engineer

What You'll Be Doing

• Model Development: Design, train, and optimize innovative machine learning models for LiDAR perception (e.g., road element detection, 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 shipping them as part of 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.

What We Need To See

• BS or 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.

Ways To Stand Out From The Crowd

• 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).

Pay

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is $184,000 - $287,500 for Level 4, and $224,000 - $356,500 for Level 5. You will also be eligible for equity and benefits.

Schedule

Applications for this job will be accepted at least until July 12, 2026.