Senior Solutions Architect, Generative AI Research

Join NVIDIA to help university researchers advance the next generation of foundation models, multimodal AI, reasoning systems, and AI agents! At NVIDIA, we build accelerated computing platforms for frontier AI research. We partner with faculty, graduate researchers, and campus research-computing teams that push model performance, efficiency, scale, and scientific impact. We are looking for a Senior Solutions Architect for our Higher Education and Research Team. This role supports academic developers working on LLMs, VLMs, pretraining, post-training, evaluation, inference studies, scalable systems, and agent behaviors such as tool use, planning, memory, and multi-agent coordination.

What you’ll be doing:

• Partner with universities to shape high-impact work on foundation models, generative AI, multimodal AI, reasoning systems, AI agents, and AI systems.

• Advise labs on GPU-accelerated training, inference studies, agent evaluation, tool-use methods, data pipelines, scaling experiments, and reproducible workflows.

• Help build research prototypes with researchers utilizing the NVIDIA full stack.

• Analyze throughput, memory, parallelism, latency, and scaling across workstations, multi-GPU servers, and campus HPC clusters.

• Translate lab feedback into technical examples, workshops, roadmap input, and adoption guidance for NVIDIA teams.

• BS, MS or PhD in Computer Science, AI/ML, Electrical Engineering, Applied Mathematics, or a related technical field, or equivalent experience.

• 8+ years of hands-on experience with AI systems, accelerated computing, distributed training, inference studies, or research-scale generative AI workflows.

• Deep foundational AI expertise across LLMs, VLMs, multimodal models, reasoning, long-context models, fine-tuning, post-training, agentic AI, and evaluation.

• Strong systems fluency in PyTorch or JAX, Python, Linux, distributed AI, data loading, checkpointing, memory optimization, batching, scheduling, latency, and throughput.

• Experience guiding faculty, graduate researchers, and research-computing teams on benchmarks, reproducibility, reliability, safety, agent evaluation, and research impact.

• Clear communication, technical judgment, and comfort turning complex model, agent, and infrastructure questions into practical next steps for labs.

Ways to stand out from the crowd:

• Advance AI scholarship through publications, open-source contributions, benchmark leadership, technical workshops, tutorials, or academic lab collaborations.

• Contribute to pretraining, post-training, RLHF/RLAIF, DPO, synthetic data, data curation, scaling laws, model efficiency, agent evaluation, or benchmark design.

• Familiarity with AI agent methods like LangGraph, LlamaIndex, LangChain, CrewAI, AutoGen, Semantic Kernel, Google ADK, OpenAI Agents SDK, DSPy, MCP, or A2A.

• Experience with NVIDIA NeMo (Agent Toolkit, Guardrails, Megatron, Framework, NIM), Nemotron, OSS, Transformer Engine, TensorRT-LLM, Triton, RAPIDS.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD.

You will also be eligible for equity and benefits (https://www.nvidia.com/en-us/benefits/) .