On-site in Santa Clara, California, United States. NVIDIA is seeking a Machine Learning Engineer to work on Product Security, Content Safety, ML Fairness and Robustness for large language models across research and production engineering teams. The role focuses on safety for multi-modal LLMs and includes developing datasets and models for Content Safety, ProdSec, Robustness and ML Fairness; researching and implementing techniques for bias detection and mitigation; defining and tracking metrics for responsible LLM behavior; and contributing to the MLOps platform to build safety tooling for engineering teams. What you’ll do: • Develop datasets and models for training and evaluating content safety, ProdSec, robustness, and ML fairness for LLMs. • Research and implement techniques for bias detection and mitigation in LLMs and systems using LLMs like RAGs. • Define and track key metrics for responsible LLM behavior. • Follow best MLOps practices for automation, monitoring, scale and safety. • Contribute to the MLOps platform and develop safety tools for ML teams. • Collaborate with engineers, data scientists, and researchers to address content safety and ML fairness challenges. What we need to see: • Master’s or PhD in Computer Science, Electrical Engineering or related field, or equivalent experience. • Minimum 2+ years of production ML development and deployment experience. • Strong understanding of ML principles and algorithms. • Proficiency in Python and deep experience with ML frameworks like Keras or PyTorch. • Background in Content Safety, ML Fairness, Robustness, AI Mode

On-site in Santa Clara, California, United States. NVIDIA is seeking a Machine Learning Engineer to work on Product Security, Content Safety, ML Fairness and Robustness for large language models across research and production engineering teams. The role focuses on safety for multi-modal LLMs and includes developing datasets and models for Content Safety, ProdSec, Robustness and ML Fairness; researching and implementing techniques for bias detection and mitigation; defining and tracking metrics for responsible LLM behavior; and contributing to the MLOps platform to build safety tooling for engineering teams. What you’ll do: • Develop datasets and models for training and evaluating content safety, ProdSec, robustness, and ML fairness for LLMs. • Research and implement techniques for bias detection and mitigation in LLMs and systems using LLMs like RAGs. • Define and track key metrics for responsible LLM behavior. • Follow best MLOps practices for automation, monitoring, scale and safety. • Contribute to the MLOps platform and develop safety tools for ML teams. • Collaborate with engineers, data scientists, and researchers to address content safety and ML fairness challenges. What we need to see: • Master’s or PhD in Computer Science, Electrical Engineering or related field, or equivalent experience. • Minimum 2+ years of production ML development and deployment experience. • Strong understanding of ML principles and algorithms. • Proficiency in Python and deep experience with ML frameworks like Keras or PyTorch. • Background in Content Safety, ML Fairness, Robustness, AI Mode