Senior Machine Learning Engineer, AI Safety

NVIDIA•Santa Clara, CA

1d•$184,000 - $356,500

About The Position

NVIDIA is seeking talented Deep Learning Scientists / AI Researchers / Machine Learning Engineers to join our rapidly growing AI Safety and Responsibility efforts for Enterprise Risk Management. In this role, you will take on innovative problems in machine learning, focusing specifically on scaling safety for multi-modal Large Language Models (LLMs) including advanced agentic safety. NVIDIA is in a unique position: we develop AI-based products across multiple domains and collaborate with the world’s leading AI companies as partners and customers. This role is directed at measuring improving the security, content safety, and inclusivity of our frontier models. Because we are expanding across multiple pillars of safety, we are looking for specialists with deep expertise in one or more of the following core focus areas: LLM Security, Frontier Risks, Agentic Safety, and Multi-turn Safety Evaluation.

Requirements

Master’s or PhD in Computer Science, Electrical Engineering, or a related quantitative field (or equivalent experience).
8+ years of proven experience in systems software engineering or machine learning engineering.
4+ years of hands-on work experience in post-training of LLMs, including Supervised Fine-Tuning (SFT), Reinforcement Learning (RLHF/RLAIF), safety data generation techniques, ablation studies, and deploying models to production.
1+ years of dedicated experience or research in at least one of the following areas: LLM Security (backdoors, poisoning, latent behaviors), Frontier Risks (deception, manipulation, loss-of-control), Agentic Safety (LLM-level risks for multi-turn tool-calling/agents), or Multi-turn Safety Evaluation (dynamic and multi-turn alignment benchmarks).
In-depth knowledge of machine learning principles and frameworks (PyTorch preferred) with strong Python programming skills.
Experience working with large multimodal datasets and multi-modal foundational models.
Outstanding analytical problem-solving abilities paired with excellent collaboration and communication skills.
Demonstrates behaviors that build trust: humility, transparency, respect, and intellectual honesty.

Nice To Haves

Published papers on AI Safety, alignment, or machine learning security as a primary author at top-tier conferences (NeurIPS, ICML, ICLR, ACL, etc.).
Active contributions to open-source AI Safety tools, benchmarks, datasets, and/or models.
Proven experience with alignment/fine-tuning of Vision-Language Models (VLMs) or any-to-text foundational models.

Responsibilities

Develop datasets and specialized models & algorithms to evaluate/benchmark models & end-to-end systems in our core safety tracks (LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness).
Develop datasets and recipes for filtering training data, developing training datasets & recipes, including components like RL environments and teacher models, across our core safety tracks (LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness).
Research & deploy new approaches, like Instruction Hierarchy or Risk Detection, for model & system level techniques beyond post-training.
Partner with engineers, data scientists, and research teams across NVIDIA to scale solutions for LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness.