Developer Relations Manager – Higher Education and Research, Foundational AI

This is a fully remote position, open to applicants in California.

📋 Description

• Act as a reliable technical consultant for prominent academic AI laboratories focused on foundation models, LLMs, multimodal AI, reasoning, training, inference, and AI systems.

• Recognize significant research workloads where NVIDIA software, systems, and accelerated computing platforms can enhance model performance, scalability, and efficiency.

• Collaborate with principal investigators, postdoctoral researchers, graduate students, and lab leadership to comprehend research objectives, technical challenges, infrastructure requirements, and opportunities for collaboration.

• Monitor cutting-edge AI research through papers, benchmarks, open-source initiatives, and academic labs to discover emerging trends and future platform possibilities.

• Work alongside Research Account Managers, Solution Architects, Product, Engineering, and Business Development teams to facilitate researcher adoption and foster long-term engagement.

• Convert academic input into practical insights for product roadmaps, developer programs, education, and platform strategies.

• Enhance NVIDIA's involvement in major AI, ML, and systems research events through technical content, workshops, university interactions, and lab-focused initiatives.

⛳️ Requirements

• Doctorate in Computer Science, AI, Machine Learning, Applied Mathematics, Electrical Engineering, or a related technical discipline, or equivalent research experience.

• Over 5 years of experience in the tech industry encompassing software engineering, developer relations, technical partnerships, solutions architecture, or product management, with at least 3 years of hands-on AI experience.

• Extensive knowledge in foundational AI topics, including LLMs, multimodal models, generative AI, reasoning, post-training, model evaluation, or AI systems research.

• Comprehensive understanding of modern AI model development throughout the lifecycle, including pretraining, fine-tuning, post-training, optimization, evaluation, deployment, and model serving.

• Practical experience with AI research stacks such as PyTorch, JAX, distributed training frameworks, inference systems, model serving platforms, evaluation pipelines, and GPU-accelerated workflows.

• Technical proficiency in scalable AI systems, including distributed training, parallelism techniques, checkpointing, memory optimization, batching, scheduling, latency, throughput, and cost-performance considerations.

• Knowledge of techniques that enhance model efficiency and performance, such as quantization, distillation, sparsity, speculative decoding, attention optimization, synthetic data generation, RLHF/RLAIF, and preference optimization.

• Capacity to engage with leading academic labs on advanced research challenges, including scaling behavior, compute efficiency, model quality, benchmark methodologies, reproducibility, reliability, and research impact.

• Proven research credibility through publications, open-source contributions, academic partnerships, technical leadership, or direct involvement with cutting-edge AI systems.

• Familiarity with NVIDIA AI platforms, including CUDA, CUDA-X libraries, TensorRT-LLM, Triton Inference Server, NIM, NeMo, Megatron, Transformer Engine, NCCL, DGX, NVLink, InfiniBand, or NVIDIA AI Enterprise.

• Established connections with reputable AI labs, academic institutions, research entities, benchmark communities, or significant open-source AI projects.

• Demonstrated ability to translate advanced AI research into demonstrations, tutorials, reference architectures, workshops, technical blogs, or developer enablement initiatives.

• Previous experience presenting at conferences such as NeurIPS, ICML, ICLR, CVPR, AAAI, or similar research workshops.

• Skill in identifying emerging research trends and transforming them into strategic opportunities for collaboration, platform adoption, and ecosystem development.

🏝️ Benefits

• Equity

• Benefits