Machine Learning Engineer, Data Curation - Autonomous Vehicles

NVIDIA•Santa Clara, CA

About The Position

NVIDIA is seeking a Machine Learning Engineer for its Autonomous Vehicles Data Curation team. The role focuses on applying state-of-the-art machine learning and deep learning techniques to search, curate, and evaluate high-quality datasets and ground truth for autonomous vehicles. The engineer will help develop datasets and tools that support perception, prediction, planning, and control for self-driving cars, with a goal of enabling full autonomy. The position involves working with deep learning and computer vision researchers and engineers to turn research into robust, production-ready machine learning models, from initial evaluation and experimentation through deployment. It also includes building vision-language approaches for scenario search and automated data curation, and developing training pipelines and scalable inference systems. NVIDIA describes this as an opportunity to contribute to autonomous driving and work with teams developing AI technologies.

Requirements

PhD with at least 1 year of relevant experience, or a master's degree (or equivalent experience) with at least 5 years of relevant experience, in Computer Science, Computer Engineering, or a related technical field.
Proven experience building robust software.
Passion for artificial intelligence for robotics and autonomous navigation.
Willingness to learn and ability to solve challenging problems.
Knowledge of mathematics.
Experience in deep learning or machine learning.
Background in computer vision and/or multimodal learning.
Programming and debugging skills in C++ and/or Python.
Good communication and analytical skills.
Ability to work with multiple teams in a dynamic environment.

Nice To Haves

Background applying the latest AI methods to computer vision and autonomous vehicle problems.
Experience with unsupervised or self-supervised learning and contrastive representation learning.
Experience with vision-language models, agentic search, or automated dataset quality evaluation.

Responsibilities

Stay current with research and innovations in deep learning, and implement and experiment with new ideas.
Collaborate with researchers and engineers to turn research into robust, industrial-strength machine learning models, from evaluation and experimentation through shipping.
Define, collect, and curate training and evaluation datasets, focusing on scenario coverage and label quality.
Develop vision-language models and embeddings for scenario search, classification, and automated data curation.
Build training pipelines and scalable inference systems using technologies such as PyTorch, TensorFlow, TensorRT, Python, and C++.