Senior Developer Relations Manager

Overview

• NVIDIA is the leading full-stack accelerated computing company, powering the next wave of generative AI, agentic AI, deep learning, data science, cloud-native AI, and edge AI.
• This role will lead hands-on Developer Relations with India's AI Labs, helping researchers, ML infrastructure teams, and startup CTOs adopt NVIDIA platforms for model development, training, optimization, deployment, and production inference.
• The ideal candidate is a senior technical DevRel leader who can earn credibility with ML researchers and platform engineers.
• This person should be comfortable reading code, writing examples, building demos, running benchmarks, explaining architecture tradeoffs, profiling workloads, and translating developer feedback into useful product input.
• What You'll Be Doing: Build and execute a technical Developer Relations strategy to grow NVIDIA platform adoption across AI Labs in India.
• Develop trusted relationships with founders, CTOs, ML researchers, ML infrastructure teams, platform leaders, and developer communities.
• Identify and accelerate high-value workloads such as foundation model training, fine-tuning, speech AI, retrieval augmented generation, multimodal AI, inference optimization, and production model serving.
• Assess which AI Lab workloads are a strong fit for GPU acceleration by profiling bottlenecks and distinguishing compute-bound problems from memory-bound, IO-bound, network-bound, or orchestration-bound issues.
• Build and adapt technical demos, sample code, notebooks, benchmark plans, reference architectures, and performance guides.
• Run deep technical workshops, code labs, architecture reviews, office hours, developer sessions, technical webinars, and executive briefings.
• Work hands-on with developers to debug integration issues, profile workloads, improve inference performance, and identify the right NVIDIA software stack for each use case.
• Explain why similar model workloads may perform differently across labs due to model architecture, data pipeline design, batch size, latency targets, storage/network behavior, software stack, or deployment environment.
• Capture developer feedback, technical blockers, competitive insights, and product 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 PyTorch, TensorFlow, 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 NeMo Framework, NeMo Curator, Nemotron model families, TensorRT-LLM, NVIDIA NIM, Triton Inference Server, CUDA, NCCL, Nsight Systems, Nsight Compute, NGC containers, and NVIDIA AI Enterprise.
• Ability to demonstrate how NeMo Framework supports model training, customization, alignment, evaluation, and deployment workflows for LLMs, multimodal models, and speech AI.
• Ability to reason about when NeMo 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, TensorRT-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 DevRel practitioner, including public talks, workshops, GitHub 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.