Senior Machine Learning Engineer, AI Safety

NVIDIA•Santa Clara, CA

4d•$184,000 - $287,500

About The Position

NVIDIA is seeking a Senior Machine Learning Engineer to advance AI Safety and Responsibility for Enterprise Risk Management. The role focuses on scaling safety for multimodal large language models (LLMs), including advanced agentic safety, and measuring and improving the security, content safety, and inclusivity of frontier models. Areas of focus include LLM security, frontier risks, agentic safety, and multi-turn safety evaluation. The engineer will develop datasets, models, and algorithms to evaluate and benchmark models and end-to-end systems across safety tracks including LLM security, agentic safety, content safety, hallucinations, and ML fairness. The role also develops data filtering and training datasets and recipes for pre-training, mid-training, and post-training, including reinforcement learning environments and teacher models. It researches and deploys techniques beyond post-training, such as instruction hierarchy and risk detection, and collaborates with engineering, data science, and research teams across NVIDIA. The position offers a base salary range of USD 184,000–287,500, determined by location, experience, and other objective job-related factors. The employee will also be eligible for equity and benefits. Applications will be accepted at least until November 6, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer committed to an inclusive work environment.

Requirements

Master’s or PhD in Computer Science, Electrical Engineering, or a related quantitative field, or equivalent experience.
At least 8 years of proven experience in systems software engineering or machine learning engineering.
At least 4 years of hands-on post-training experience with LLMs, including supervised fine-tuning, reinforcement learning from human or AI feedback, safety data generation techniques, ablation studies, and production model deployment.
At least 1 year of dedicated experience or research in at least one of: LLM security, frontier risks, agentic safety, or multi-turn safety evaluation.
In-depth knowledge of machine learning principles and frameworks, with PyTorch preferred, and strong Python programming skills.
Experience working with large multimodal datasets and multimodal foundational models.
Strong analytical problem-solving, collaboration, and communication skills.
Demonstrated humility, transparency, respect, and intellectual honesty.

Nice To Haves

Primary-author publications on AI safety, alignment, or machine learning security at top-tier conferences such as NeurIPS, ICML, ICLR, or ACL.
Active contributions to open-source AI safety tools, benchmarks, datasets, or models.
Experience with alignment or fine-tuning of vision-language models or any-to-text foundational models.

Responsibilities

Develop datasets, specialized models, and algorithms to evaluate and benchmark models and end-to-end systems across LLM security, agentic safety, content safety, hallucinations, and ML fairness.
Develop datasets and recipes for filtering training data and for pre-training, mid-training, and post-training, including reinforcement learning environments and teacher models, across core safety tracks.
Research and deploy model- and system-level approaches beyond post-training, such as instruction hierarchy and risk detection.
Collaborate with engineers, data scientists, and research teams across NVIDIA to scale safety solutions.