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.
Travel up to 20%.
What we need to see:
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.
We are excited to meet researchers and builders who raise the technical bar and help universities move faster from idea to discovery!
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.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- BS, MS, or PhD in Computer Science, AI/ML, Electrical Engineering, Applied Mathematics, or related field, or equivalent experience.
- 8+ years hands-on experience with AI systems, accelerated computing, distributed training, inference studies, or research-scale generative AI workflows.
- Deep expertise in 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 advising faculty, graduate researchers, or research-computing teams on benchmarks, reproducibility, reliability, safety, and agent evaluation.
- Clear communication skills and technical judgment to translate complex model, agent, and infrastructure questions into practical next steps.
- Publications, open-source contributions, benchmark leadership, tutorials, or academic collaborations.
- Familiarity with AI agent frameworks (e.g., LangGraph, LlamaIndex, LangChain, AutoGen, Semantic Kernel, OpenAI Agents SDK).
- Experience with NVIDIA AI stack (e.g., NeMo, Nemotron, Transformer Engine, TensorRT-LLM, Triton, RAPIDS).
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
-
Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
-
Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
-
Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
