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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Solutions Architect, Generative AI Research - **Company:** NVIDIA Ltd. - **Location:** United States - **Experience:** Expert - **Salary:** $184,000.0 - $287,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computing Platforms, Program Optimization, Extract Transform Load (ETL), Linux, Distributed Computing Environment, Python (Programming Language), Machine Learning, Pytorch, Large Language Models, Multi-Agent Systems, Parallel Computation, Generative AI, Information Technology, Low Latency, TensorRT, Virtual Agents, Nim (Programming Language), Data Pipelines - **Published:** June 26, 2026 - **Apply:** https://www.juju.com/job/00000000gb4000 ## About the Role + 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. ## Description 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. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Your Next AI Needs 10,000 GPUs. Now What?](https://www.wearedevelopers.com/videos/1590-your-next-ai-needs-10-000-gpus-now-what) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)