Senior AI Engineer, AI Safety

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in an inclusive, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. Serving as an AI Engineer on our AI Safety Engineering team, you will have a key and adaptable role in scaling methods to secure and align advanced AI systems. Your early work will center on architecting and developing the fundamental elements of our safety infrastructure, emphasizing autonomous automation cycles. To operationalize these automated workflows, you will partner with Research and Model Development teams thereby delivering novel models for harm detection, content moderation, dynamic guardrails, post-training, and groundbreaking automated research pipelines that push the limits. This role is built for a tenacious builder and an inquisitive problem solver. We want an engineer who thrives in ambiguity. They must have the grit to overcome complex technical challenges and transform innovative safety concepts from raw ideas to scalable implementations. What You’ll Be Doing: • Architect Agentic Automation: Compose, build, and deploy foundational agentic automation loops that streamline workflows spanning safety evaluations, red-teaming, model testing, model post-training and ablations. • Develop Safety Models: Move from infrastructure automation towards training and implementing specialized systems for identifying harmful content, coordinating content quality, and run-time guard-railing. • Pioneer Auto-Research: Build and guide automated research frameworks. These frameworks use LLMs and autonomous agents to discover new vulnerabilities. They also generate or augment safety datasets for benchmarking or training and propose novel alignment techniques. • Scale Innovations: Take proof-of-concept safety interventions and research ideas and engineer them into robust, highly scalable solutions that integrate seamlessly into our production pipelines. • Cross-Functional Execution: Partner closely with researchers, platform engineers, and data scientists to bridge the gap between theoretical AI safety research and practical, technically focused automated systems. What We Need To See: • Education: Bachelor’s degree or Master’s degree with equivalent experience in Computer Science, Machine Learning, Software Engineering, or a related field.  • Experience: Candidates should have a strong track record in ML engineering or software engineering with 8+ years of experience. • Agentic Systems & Automation: Proven experience building complex, automated workflows using LLMs, including multi-turn tool-calling, orchestration, and autonomous agent loops. • Strong engineering fundamentals include deep proficiency in one or more languages such as Rust or Python. Experience with machine learning frameworks like PyTorch is also required. The focus is on writing scalable, maintainable, and highly optimized code. • Inquisitive Problem-Solving & Tenacity: A demonstrated ability to tackle open-ended, ambiguous problems. You must be willing to experiment, fail, iterate, and relentlessly pursue solutions. • End-to-End Execution: The ability to own a project from the initial white-boarding of a research idea to the final deployment of a scalable production sy