Multimodal LLM Safety Engineer — Content & Fairness

Remote Jobs; United States; À distance; USD 124 000 - 242 000; Plein temps

Résumé du poste

NVIDIA is seeking a talented Machine Learning Engineer to work on Product Security, Content Safety, ML Fairness and Robustness for LLMs across research and production teams. You will develop datasets and models for training and evaluating safety-focused systems, and you will quantify and improve model safety and inclusivity at scale.

The role emphasizes collaboration across engineers, data scientists, and researchers to advance bias detection, alignment, and safe deployment practices.

Qualifications

• Master’s or PhD in Computer Science, Electrical Engineering or related field (or equivalent experience).
• Minimum of 2+ years of work experience in developing and deploying ML models in production.
• Strong understanding of ML principles and algorithms.
• Hands-on programming in Python with ML frameworks (PyTorch / Keras).
• Experience in Content Safety, ML Fairness, Robustness, or AI Model Security is a plus.

Responsabilités

• Develop datasets and models for training and evaluating safety-focused LLMs and multi-modal systems.
• Research and implement bias detection and mitigation techniques in LLMs and RAG-based systems.
• Define and track metrics for responsible LLM behavior and usage.
• Follow MLOps practices for automation, monitoring, scale, and safety.
• Collaborate with engineers and researchers to address content safety and fairness challenges.

Connaissances

Python programming

ML fundamentals

Collaboration & communication

Analytical thinking

Trust & integrity

Formation

Master’s or PhD in CS/EE or related field

Outils

PyTorch

Keras

Description du poste

NVIDIA is seeking a talented Machine Learning Engineer to work on Product Security, Content Safety, ML Fairness and Robustness for LLMs across research and production teams. You will develop datasets and models for training and evaluating safety-focused systems, and you will quantify and improve model safety and inclusivity at scale.

The role emphasizes collaboration across engineers, data scientists, and researchers to advance bias detection, alignment, and safe deployment practices.