Senior Solutions Architect, Generative AI Research

About the Role

The role involves travel up to 20% of the time, indicating a need for strong communication and interpersonal skills to effectively collaborate with researchers and teams across different locations. The ability to analyze complex systems, optimize performance, and ensure reproducibility of workflows will be essential in this position.

NVIDIA is committed to pushing the boundaries of AI research and has developed a range of tools and platforms to support this effort. The company's focus on accelerated computing, distributed training, and inference studies provides a dynamic environment for professionals to grow and contribute to the advancement of AI systems.

What You Will Do

• 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.
• Collaborate with internal teams to ensure the effective use of NVIDIA technologies in research environments.
• Develop and maintain strong relationships with key stakeholders in the academic and research communities.
• Stay updated on the latest advancements in AI research and technologies, applying this knowledge to improve existing processes and workflows.
• Participate in the development of technical documentation, workshops, and training materials to support the adoption of NVIDIA technologies.

What We Are Looking For

• A 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.
• Ability to work effectively in a remote environment, with strong self-motivation and discipline.

Nice to Have

• 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.

Benefits and Perks

• Highly competitive salaries determined based on location, experience, and internal equity.
• Eligibility for equity and a comprehensive benefits package.
• Access to cutting-edge technologies and the opportunity to contribute to the development of AI systems.
• Collaboration with a talented team of professionals in a dynamic and inclusive work environment.
• Remote work flexibility with up to 20% travel for collaboration and research partnerships.
• Opportunities for professional growth and development through training, workshops, and conferences.
• A culture that values diversity, equity, and inclusion, with a commitment to fostering a welcoming workplace for all employees.

• Ensure you have a strong foundation in AI systems, accelerated computing, and research-scale generative AI workflows to stand out in your application.
• Highlight any experience guiding research teams on benchmarks, reproducibility, and reliability, as these skills are highly valued in this role.
• Familiarize yourself with NVIDIA's technologies, such as NVIDIA NeMo and NVIDIA TensorRT, to demonstrate your ability to adapt to the company's ecosystem.
• Prepare to discuss complex model and infrastructure questions, showcasing your ability to communicate technical concepts effectively.
• Consider creating a portfolio that showcases your contributions to AI research, including publications, open-source projects, or presentations, to demonstrate your expertise and commitment to the field.
• Be prepared to discuss your experience with remote collaboration tools and your strategies for effective communication in distributed teams.