Senior Developer Relations Manager

requirements for NVIDIA product and engineering teams. What We Need To See: Bachelor's degree in engineering, computer science, data science, or a related technical discipline, or equivalent experience and 8+ years of experience in technical Developer Relations, ML engineering, AI platforms, model infrastructure, cloud platforms, solution architecture, or startup technical engagement. Strong understanding of generative AI, deep learning, machine learning, model lifecycle, inference serving, model optimization, distributed training, and MLOps. Hands-on programming experience in Python, with working familiarity in C++, CUDA, or other performance-oriented development environments preferred. Experience with modern AI frameworks and deployment tooling such as Py Torch, Tensor Flow, JAX, Hugging Face, model serving systems, containers, Kubernetes, APIs, CI/CD, and distributed application architecture. Ability to profile and reason about AI workloads, including GPU suitability, compute intensity, memory bandwidth, IO bottlenecks, network constraints, latency, throughput, batching, and cost/performance tradeoffs. Ability to engage both deeply technical developers and senior business stakeholders, with credibility in whiteboarding, live demos, technical troubleshooting, and architecture reviews. Hands-on exposure to core NVIDIA AI software and SDKs relevant to model builders, including Ne Mo Framework, Ne Mo Curator, Nemotron model families, Tensor RT-LLM, NVIDIA NIM, Triton Inference Server, CUDA, NCCL, Nsight Systems, Nsight Compute, NGC containers, and NVIDIA AI Enterprise. Ability to demonstrate how Ne Mo Framework supports model training, customization, alignment, evaluation, and deployment workflows for LLMs, multimodal models, and speech AI. Ability to reason about when Ne Mo Curator or related data curation workflows can improve training data quality through filtering, deduplication, formatting, synthetic data workflows, and multimodal data preparation. Ability to build or adapt demos that show how Nemotron models, NIM microservices, Tensor RT-LLM, and Triton can move a model from experimentation to optimized inference. Ways To Stand Out from the crowd: Hands-on experience with large-scale model training, fine-tuning, inference optimization, or AI platform engineering. Hands-on experience with inference optimization and model efficiency techniques such as quantization, distillation, pruning, speculative decoding, batching, KV cache optimization, RLHF, RLAIF, DPO, or other post-training and alignment methods. Experience creating workload qualification frameworks, benchmark plans, or technical decision guides that help developers decide when GPU acceleration is the right fit. Track record as a technical Dev Rel practitioner, including public talks, workshops, Git Hub samples, blogs, tutorials, reference architectures, or developer community programs. Strong point of view on India's sovereign AI, Indic language AI, and foundation model ecosystem. NVIDIA pioneered accelerated computing. Today, our AI infrastructure powers global intelligence, transforming every industry. Learn more about NVIDIA.