Senior Solutions Architect, Generative AI Research

🔥 1 minute ago

🐊 Florida, North Carolina, +1 more states – Remote

🐊 Florida – Remote 🌲 North Carolina – Remote 🌴 South Carolina – Remote

💵 $184k - $287.5k / year

⏰ Full Time

🟠 Senior

💻 Solutions Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🤖 Artificial Intelligence

🎮 Gaming

Artificial Intelligence • Gaming • Automotive

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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%

🎯 Requirements

• 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

🏖️ Benefits

• Highly competitive salaries • Comprehensive benefits package • Eligibility for equity • Health insurance • Professional development opportunities