ML Engineer: AI Safety & LLM Fairness

NVIDIA; Santa Clara (CA); On-site; USD 124,000 - 242,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA in Santa Clara, CA is seeking a Machine Learning Engineer to advance safety, content safety, ML fairness and robustness for LLMs across research and production. You will build datasets and evaluation frameworks, apply bias detection and mitigation, and define metrics for responsible model behavior.

You should have a master’s or PhD, 2+ years deploying ML in production, strong Python, and experience with PyTorch/Keras.

Qualifications

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

Responsibilities

• Develop datasets and models for training and evaluating Content Safety, ProdSec, Robustness and ML Fairness systems.
• Research and implement bias detection and mitigation techniques for LLMs and related systems.
• Define and track metrics for responsible LLM behavior and usage.
• Follow MLOps practices including automation, monitoring, and safety.
• Contribute to the MLOps platform and build safety tooling for ML teams.
• Collaborate with engineers, data scientists, and researchers to address safety and fairness challenges.

Skills

Python programming

ML fundamentals

Collaboration

Problem solving

Education

Master’s or PhD in Computer Science or Electrical Engineering

Tools

Keras

PyTorch

Job description

NVIDIA in Santa Clara, CA is seeking a Machine Learning Engineer to advance safety, content safety, ML fairness and robustness for LLMs across research and production. You will build datasets and evaluation frameworks, apply bias detection and mitigation, and define metrics for responsible model behavior.

You should have a master’s or PhD, 2+ years deploying ML in production, strong Python, and experience with PyTorch/Keras.