2 days ago
Responsibilities
- Implement and experiment with new deep learning research and techniques.
- Collaborate with researchers and engineers to turn research into robust production machine learning models.
- Take machine learning approaches from evaluation and experimentation through shipping.
- Define, collect, and curate training and evaluation datasets focused 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 for autonomous vehicle applications.
Requirements
- PhD with 1+ year of relevant experience, or MS or equivalent experience with 5+ years, in Computer Science, Computer Engineering, or a related technical field.
- Proven experience building robust software.
- Experience with deep learning or machine learning, especially computer vision or multimodal learning.
- Programming and debugging skills in C++ and/or Python.
- Knowledge of mathematics and strong analytical and communication skills.
- Ability to work with multiple teams in a dynamic environment.
- Preferred: experience applying AI to computer vision and autonomous vehicle problems.
- Preferred: experience with unsupervised or self-supervised learning and contrastive representation learning.
- Preferred: experience with vision-language models, agentic search, or automated dataset-quality evaluation.
Benefits
- Competitive salary and comprehensive benefits package.
- Eligibility for equity and benefits.
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.