Machine Learning Engineer, AI Safety

NVIDIA•Us, CA

About The Position

NVIDIA is seeking a talented Machine Learning Engineer to focus on Product Security, Content Safety, ML Fairness, and Robustness for Large Language Models (LLMs). This role involves developing and improving safety and inclusivity of LLM models in a scalable manner, addressing challenges in areas like bias, discrimination, robustness, and explainability. The position offers the opportunity to work on innovative machine learning problems, particularly concerning safety for multi-modal LLMs, and contribute to ensuring LLMs are used safely and responsibly across NVIDIA's research and production engineering teams.

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 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.
Practice working with large multi-modal datasets and multi-modal models.
Good problem-solving and analytical ability.
Excellent collaboration and communication skills.
Demonstrates behaviors that build trust: humility, transparency, respect, and intellectual honesty.

Nice To Haves

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.

Responsibilities

Develop 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 best MLOps practices for automation, monitoring, scale, and safety.
Contribute to the MLOps platform and develop safety tools to enhance ML team effectiveness.
Collaborate with engineers, data scientists, and researchers to develop and implement solutions for content safety and ML fairness challenges.