About the job
At NVIDIA, we redefine what’s possible in digital imaging, personal computer gaming, and high-performance computing. Now, we lead the charge into AI’s unlimited potential. Our GPUs act as the brains of computers, robots, and self-driving cars. They enable these machines to understand and interact with the world in new ways. This is your chance to join a legacy of innovation and excellence. Work with the world’s best talent in a diverse and encouraging environment. Join us and make a lasting impact on the world!
This is an ambitious opportunity to work at the forefront of AI safety and contribute to groundbreaking advancements in technology.
Responsibilities
Lead cross-functional planning and execution for evaluation identification, selection and execution with a focus on agentic-safety evaluations.
Track execution, results for all evals, analysis, and mitigation and research plans for improvement stemming from results analysis.
Lead end-to-end safety planning and execution for Nemotron models, including program scope, dependencies, risks, resources, and release-readiness criteria.
Translate technical safety priorities into executable programs covering LLM security, frontier risks, agentic safety, and hallucinations.
Establish and track multi-turn, multi-modal, multi-lingual, long-context, and reasoning model evaluations across relevant use cases, domains, languages, modalities, and model releases.
Establish governance for reviewing safety findings, assigning severity, determining release impact, and escalating unresolved risks.
Build and maintain dashboards and executive reporting for evaluation coverage, critical findings, mitigation progress, residual risk, and release readiness.
Qualifications
Minimum
Bachelor’s degree or equivalent experience in computer science, engineering, data science, or a related technical field.
10+ years of experience in technical program management, engineering, product development, technical operations, or a similar area.
Strong understanding of LLM architecture, frameworks (e.g., OpenAI, Anthropic, Hugging Face), and model evaluation.
Familiarity with LLM development, post-training, inference, tool calling, evaluation datasets, and model-release lifecycles.
Experience directing programs related to AI/ML development, model evaluation, agentic safety, security, content safety, hallucinations, and/or production releases.
Familiarity with AI safety risks such as hallucinations, timely injection attacks, unsafe tool use, data poisoning, model manipulation, and unintended agent behavior.
Experience supporting model-safety evaluations, Red Teaming, adversarial testing, security assessments, or responsible-AI programs.
Ability to interpret technical evaluation findings and communicate their product, schedule, and release implications to leadership.
Preferred
Experience with LLM Agentic Safety, Security, Red Teaming, Hallucinations, adversarial assessment, or Responsible AI.
Experience defining evaluation datasets, rubrics, benchmarks, and release gates.
Ability to use evaluation results and data to make clear release recommendations and communicate residual risk to leadership.
Experience crafting or operationalizing multi-turn, hallucination, adversarial, or agentic-safety evaluations.