Senior Deep Learning Engineer, Accuracy Evaluation

We are seeking senior engineers to pioneer new methodologies for accurately assessing the performance and capabilities of ground-breaking deep learning models, including LLMs, RAG, agents, and vision models. You will collaborate across the organization to bring the latest flagship models from our community and partners to life. This role offers an outstanding opportunity to craft the future of AI at a fast-growing company at the forefront of the AI revolution. Join our team of world-class software engineers and partners to deliver the most advanced models with lightning-fast inference. You'll work on the most powerful, enterprise-grade GPU clusters capable of hundreds of PetaFLOPS and gain early access to unreleased hardware, making a direct impact on NVIDIA 's roadmap and the broader AI landscape! What you’ll be doing: Design and build decision-grade evaluation environments for NVIDIA 's frontier models spanning reasoning, multimodal, long-context, and agentic systems, producing auditable accuracy signals that gate every major model release. Research and develop novel evaluation methodologies for emerging model families and capability domains (low-precision numerics, multi-turn agentic tasks, code generation) where established benchmarks don't yet exist or don't generalize. Build and operate the evaluation infrastructure and pipelines including benchmark environments, regression CI systems, and statistical analysis tooling, used by model, product, and applied research teams across NVIDIA . Partner with model research, training, and customer teams to translate evaluation signals into concrete decisions: release go/no-go, training iteration direction, and competitive positioning against external frontier models. What we need to see: BS, MS, or PhD in Computer Science, Machine Learning, Statistics, or a related field 6+ years of hands-on experience with LLMs, designing and running evaluations for large language models or multimodal AI systems, including experience with