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.
The ideal candidate brings deep technical credibility in foundational AI, strong research engagement experience, and ecosystem knowledge.
What you'll be doing:
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.
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, ML, and systems research venues through technical content, workshops, university engagements, and lab-facing programs.
What we need to see:
PhD in Computer Science, AI, Machine Learning, Applied Mathematics, Electrical Engineering, or a related technical field, or equivalent research depth.
8+ years of experience
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.
Ways to stand out from the crowd:
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.
NVIDIA is widely considered to be one of the technology world’s most desirable employers! We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you. NVIDIA is committed to foster a diverse work environment and proud to be an equal opportunity employer!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 170,000 CAD - 220,000 CAD for Level 4, and 225,000 CAD - 275,000 CAD for Level 5.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
Skills Required
- PhD in Computer Science, AI, Machine Learning, Applied Mathematics, Electrical Engineering, or related field, or equivalent research depth.
- 8+ years of relevant experience.
- Deep expertise in foundational AI (LLMs, multimodal models, generative AI, reasoning, post-training, model evaluation, or AI systems research).
- Strong understanding of AI model development lifecycle: 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: distributed training, parallelism strategies, checkpointing, memory optimization, batching, scheduling, latency/throughput, and cost-performance tradeoffs.
- Familiarity with methods improving model efficiency/performance (quantization, distillation, sparsity, speculative decoding, attention optimization, synthetic data generation, RLHF/RLAIF, preference optimization).
- Ability to engage top academic labs and understand research challenges: scaling behavior, compute efficiency, benchmark methodology, reproducibility, and reliability.
- Demonstrated research credibility via publications, open-source contributions, academic collaborations, technical leadership, or direct work on frontier AI systems.
- Experience with NVIDIA AI platforms (CUDA, CUDA-X, TensorRT-LLM, Triton, NIM, NeMo, Megatron, Transformer Engine, NCCL, DGX, NVLink, InfiniBand, NVIDIA AI Enterprise).
- Established relationships with leading AI labs, academic institutions, research institutes, benchmark communities, or major open-source AI projects.
- Track record translating research into demos, tutorials, reference architectures, workshops, technical blogs, or developer enablement programs.
- Experience presenting at major research venues (NeurIPS, ICML, ICLR, CVPR, AAAI) or related workshops.
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
