ML Safety Engineer for LLMs — Fairness, Robustness & Equity

NVIDIA Corporation; Santa Clara (CA); On-site; USD 125,000 - 240,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a talented Machine Learning Engineer to advance Content Safety, ML Fairness, and Robustness for LLMs across research and production teams. You will develop datasets and models for safety, bias detection, and reliability in multi-modal systems, collaborating with engineers and researchers to improve model behavior and governance.

The role emphasizes MLOps, scalability, and responsible AI practices, with opportunities to impact Safe AI at scale across products and platforms.

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 machine learning models in production.
• Strong understanding of machine learning principles and algorithms.

Responsibilities

• Develop datasets and models for training and evaluating models for Content Safety, ProdSec, Robustness and ML Fairness.
• Research and implement techniques for bias detection and mitigation in LLMs and systems using LLMs like RAGs.
• Define and track metrics for responsible LLM behavior and usage.
• Follow best MLOps practices of automation, monitoring, scale and safety.
• Contribute to the MLOps platform and develop safety tools to help ML teams.
• Collaborate with engineers, data scientists, and researchers to address content safety and ML fairness challenges.

Skills

Python

PyTorch

Keras

ML fundamentals

Communication skills

Problem solving

Collaboration

Education

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

Tools

PyTorch

Keras

Job description

NVIDIA is seeking a talented Machine Learning Engineer to advance Content Safety, ML Fairness, and Robustness for LLMs across research and production teams. You will develop datasets and models for safety, bias detection, and reliability in multi-modal systems, collaborating with engineers and researchers to improve model behavior and governance.

The role emphasizes MLOps, scalability, and responsible AI practices, with opportunities to impact Safe AI at scale across products and platforms.