Developer Relations Manager, Higher Education and Research - Foundational AI
$152k - $288k • Remote • US, CA, Santa Clara
Posted 28d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Engineer
About the job
We are seeking a mission-driven Developer Relations Manager focused on Foundational AI Research to engage leading academic labs advancing the next generation of AI models, systems, and methods. In this role, you will work directly with top researchers building frontier AI systems, including large language models, multimodal models, reasoning systems, training methods, inference systems, model serving, and scalable AI infrastructure. You will help researchers adopt NVIDIA’s AI and accelerated computing platforms to push the boundaries of model performance, efficiency, and scale.
Responsibilities
- Serve as a trusted technical advisor to leading academic AI labs on foundation models, LLMs, multimodal AI, reasoning, training, inference, and AI systems.
- Identify high-impact research workloads where NVIDIA's platforms can advance model performance, scale, and efficiency.
- Engage with researchers and lab leadership to understand their goals, technical blockers, infrastructure needs, and collaboration opportunities.
- Track frontier AI research to identify emerging trends and future platform opportunities.
- Partner with internal teams to support researcher adoption and long-term engagement.
- Represent researcher needs internally by translating academic feedback into actionable insights for product roadmaps and strategy.
- Support NVIDIA's participation in major AI/ML research venues through technical content and engagements.
Requirements
- PhD in Computer Science, AI, Machine Learning, Applied Mathematics, Electrical Engineering, or equivalent research experience.
- 5+ years of experience in the technology industry (software engineering, developer relations, technical partnerships, etc.), including 3+ years of hands-on experience in AI.
- Deep expertise in foundational AI, including LLMs, multimodal models, generative AI, reasoning, post-training, model evaluation, or AI systems research.
- Strong understanding of the AI model development lifecycle (pretraining, fine-tuning, post-training, optimization, evaluation, deployment, model serving).
- Hands-on experience with AI research stacks (e.g., PyTorch, JAX, distributed training, inference systems, model serving platforms, evaluation pipelines, GPU-accelerated workflows).
- Technical fluency in scalable AI systems (distributed training, parallelism, checkpointing, memory optimization, batching, scheduling, latency, throughput, cost-performance tradeoffs).
- Familiarity with methods for improving model efficiency and performance (e.g., quantization, distillation, sparsity, RLHF/RLAIF).
- Ability to engage top academic labs on frontier research challenges.
- Demonstrated research credibility through publications, open-source contributions, academic collaborations, or direct work on frontier AI systems.
Benefits
- Equity
- Benefits