Senior Machine Learning Engineer

NVIDIA•Santa Clara, CA

1d•$184,000 - $356,500

About The Position

Intelligent machines powered by Artificial Intelligence computers that can learn, reason, and interact with people are no longer science fiction. Today, a self-driving vehicle powered by AI can meander through a country road at night and find its way. NVIDIA's GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots, and self-driving vehicles that can perceive and understand the world. 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 in the unconstrained world under any conditions? If so, join us!

Requirements

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.

Nice To Haves

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).

Responsibilities

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.
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++.
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.