Machine Learning Engineer, AI Safety

NVIDIA develops AI-based products across multiple domains and focuses on ensuring that generative AI systems are safe, fair, robust, and explainable. The Machine Learning Engineer will develop datasets, models, metrics, MLOps capabilities, and safety tools for content safety, product security, ML fairness, and robustness in large language and multimodal models.

Responsibilities:

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

Requirements:

• 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
• Hands-on programming experience in python and in-depth knowledge of machine learning frameworks, like Keras or PyTorch
• Background in one or more of the following broader areas for 1+ years: Content Safety, ML Fairness, Robustness, AI Model Security, or related areas
• Experience working in a range of the following areas within Content Safety: Hate/Harassment, Sexualized, Harmful/Violent, or other specific areas from your application
• Practice working with large multi-modal datasets and multi-modal models
• Good at problem-solving and analytical ability
• Excellent collaboration and communication skills
• Demonstrates behaviors that build trust: humility, transparency, respect, and intellectual honesty
• Skilled with alignment/fine-tuning of LLMs - including regular LLMs as well as VLMs (vision-language models) or any-to-text
• Proven experience with multimodal and/or multilingual content safety, legal, and regulatory compliance
• Knowledge of robustness, including hallucinations, digressions, and generative misinformation
• Experience with GenAI security, including prompt stability, model extraction, confidentiality/data extraction, integrity, availability, and adversarial robustness
• Passion for AI and a demonstrated commitment to advancing the field through innovative research, prior scientific research, and publication experience