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NVIDIA

Designs the chips and software that power AI

Developer Relations Manager - Higher Education and Research, Foundational AI

Developer Relations & AdvocacyConfirmed live in the last 24 hours
Full-timeRemote

About the Job

Requirements

  • A PhD in Computer Science, AI, Machine Learning, Applied Mathematics, Electrical Engineering, or a related technical field, or equivalent research experience.
  • At least 5 years of technology-industry experience across software engineering, developer relations, technical partnerships, solutions architecture, or product management, including at least 3 years of hands-on AI experience.
  • Deep expertise in foundational AI, including large language models, multimodal models, generative AI, reasoning, post-training, model evaluation, or AI systems research.
  • Strong understanding of modern AI model development across pretraining, fine-tuning, post-training, optimization, evaluation, deployment, and model serving.
  • Hands-on experience with AI research stacks such as PyTorch, JAX, distributed training frameworks, inference systems, model serving platforms, evaluation pipelines, and GPU-accelerated workflows.
  • Technical fluency in scalable AI systems, including distributed training, parallelism strategies, checkpointing, memory optimization, batching, scheduling, latency, throughput, and cost-performance tradeoffs.
  • Familiarity with methods that improve model efficiency and performance, such as quantization, distillation, sparsity, speculative decoding, attention optimization, synthetic data generation, reinforcement learning from human or AI feedback, and preference optimization.
  • Ability to engage leading academic labs on frontier research challenges, including scaling behavior, compute efficiency, model quality, benchmark methodology, reproducibility, reliability, and research impact.
  • Demonstrated research credibility through publications, open-source contributions, academic collaborations, technical leadership, or direct work on frontier AI systems.

Responsibilities

  • Serve as a trusted technical advisor to leading academic AI labs working on foundation models, large language models, multimodal AI, reasoning, training, inference, and AI systems.
  • Identify high-impact research workloads where NVIDIA software, systems, and accelerated computing platforms can advance model performance, scale, and efficiency.
  • Engage principal investigators, postdoctoral researchers, graduate researchers, and lab leadership to understand research goals, technical blockers, infrastructure needs, and collaboration opportunities.
  • Track frontier AI research across papers, benchmarks, open-source projects, and academic labs to identify emerging trends and future platform opportunities.
  • Partner with Research Account Managers, Solution Architects, Product, Engineering, and Business Development teams to support researcher adoption and long-term engagement.
  • Represent researcher needs internally by translating academic feedback into actionable insights for product roadmaps, developer programs, education, and platform strategy.
  • Support NVIDIA participation in major AI, machine learning, and systems research venues through technical content, workshops, university engagements, and lab-facing programs.

Desired Qualifications

  • Experience 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 relationships with leading AI labs, academic institutions, research institutes, benchmark communities, or major open-source AI projects.
  • Track record of translating frontier AI research into demos, tutorials, reference architectures, workshops, technical blogs, or developer enablement programs.
  • Experience presenting at venues such as NeurIPS, ICML, ICLR, CVPR, AAAI, or related research workshops.
  • Ability to identify emerging research trends and convert them into strategic opportunities for collaboration, platform adoption, and ecosystem growth.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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