Developer Relations Manager – Higher Education and Research, Foundational AI

NVIDIA

. Serve as a trusted technical advisor to leading academic AI labs working on foundation models, LLMs, multimodal AI, reasoning, training, inference, and AI systems.

Posted 9/21/2026full-timeRemote • California • United StatesMid-LevelSenior💰 $152,000 - $241,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep expertise in foundational AI, including LLMs and multimodal models, with hands-on experience in AI research stacks and scalable AI systems. Proven ability to engage with academic labs and translate research insights into actionable strategies for product development and collaboration.

Highest-signal resume keywords

PhD In Computer Science5+ Years Experience In AIDeep Expertise In Foundational AIHands-On Experience With PyTorchExperience With NVIDIA AI Platforms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI ResearchModel EvaluationModel OptimizationDistributed TrainingModel ServingQuantizationDistillationAttention OptimizationSynthetic Data GenerationTechnical Leadership

Soft Skills

Engagement With Academic LabsCollaboration OpportunitiesResearch CredibilityCommunication SkillsTranslating Feedback

Tools & Technologies

CUDATensorRT-LLMTriton Inference ServerNeMoMegatronTransformer EngineNCCLDGXNVLinkInfiniBand

Industry Keywords

AI SystemsMachine LearningGenerative AIResearch WorkloadsBenchmark MethodologyOpen-Source ContributionsFrontier AI ResearchDeveloper RelationsTechnical PartnershipsEcosystem Growth

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

Serve as a trusted technical advisor to leading academic AI labs working on foundation models, LLMs, 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, postdocs, 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.
Translate academic feedback into actionable insights for product roadmaps, developer programs, education, and platform strategy.
Support NVIDIA participation in major AI, ML, and systems research venues through technical content, workshops, university engagements, and lab-facing programs.

Requirements

What you’ll need

PhD in Computer Science, AI, Machine Learning, Applied Mathematics, Electrical Engineering, or a related technical field, or equivalent research experience.
5+ years of experience in the technology industry across software engineering, developer relations, technical partnerships, solutions architecture, or product management, 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 modern AI model development across the lifecycle, including 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, RLHF/RLAIF, and preference optimization.
Ability to engage top 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.
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 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.

Benefits

Comp & perks

Equity
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