Senior AI And ML Engineer — AI For Networking

Details

NVIDIA redefines what’s possible. NVIDIA has been reinventing computer graphics, PC gaming, and accelerated computing for 30 years. It is a unique legacy of innovation fueled by great technology and amazing people. Today, NVIDIA taps into the unlimited potential of AI to define the next era of computing — where GPUs act as the brains of computers, generative AI, robots, and self-driving cars that understand the world.

Our company is at the forefront of technological innovation, dedicated to driving efficiency and optimizing infrastructure performance both on-prem and in the cloud. Join us in this exciting endeavor!

Responsibilities

• Architect and implement infrastructure platforms tailored for AI/ML workloads, focusing on scaling private cloud environments for high-throughput training, inference, and Agentic workflows/pipelines.
• Lead initiatives in Generative AI system design including Retrieval-Augmented Generation (RAG), LLM fine-tuning, semantic search, and multi-modal data processing.
• Build and optimize ML systems for document understanding, vector-based retrieval, and knowledge graph integration using advanced NLP and information retrieval techniques.
• Design and develop scalable services and tools supporting GPU-accelerated AI pipelines leveraging Kubernetes, Python/Go, and observability frameworks.
• Mentor and collaborate with a multidisciplinary team of network engineers, automation engineers, AI/ML scientists, product managers, and domain experts.
• Build and drive adoption of emerging AIOPs technologies by integrating AI Agents, RAGs, and LLMs using MCP workflows for automation, performance tuning, and large-scale data insights.

Requirements

• 10+ years of engineering experience with at least 5 years leading ML infrastructure, AI systems, or applied NLP/LLM development initiatives.
• 5+ years experience in Networking and infrastructure.
• Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, Machine Learning, or related field (or equivalent experience).
• Deep expertise with generative AI concepts like embeddings, RAG, semantic search, transformer-based LLMs.
• Experience with MCP workflows and Agentic ecosystem.
• Knowledge of vector databases (FAISS, Pinecone, Weaviate) and data pipelines.
• Programming proficiency in Python (preferred) and/or Go, with software engineering best practices.
• Experience deploying and tuning LLMs using LoRA, QLoRA, instruction tuning.
• Strong understanding of infrastructure automation pipelines (Terraform, Ansible, Salt), monitoring (Prometheus, Grafana), and DevOps tools.
• Hands-on experience with petabyte-scale datasets, schema design, and distributed processing.
• Strong background in infrastructure-related data collections and logs related to network data; ability to run simulations of network state with AI tools.

Ways to Stand Out

• Experience building multi-hop RAG systems with self-consistency and chain-of-thought prompting.
• Leadership in designing AI platforms for large-scale enterprise search, document intelligence, or recommendation systems.
• Contributions to open-source ML/AI tools or active participation in AI research community.
• Familiarity with knowledge graph construction and reasoning systems; demonstrated ability to communicate complex ML concepts to stakeholders.
• Strong knowledge of automation pipeline and observability tools like BigPanda, Splunk, Storm, Netbox/Nautobot, and open-source automation tools.
• Strong knowledge of network operating systems like Arista EOS, Cumulus, Cisco NX-OS, Sonic, SRLinux and expertise in Infrastructure or Network as Code automation frameworks.

NVIDIA is committed to a diverse work environment and is proud to be an equal opportunity employer. Creative and autonomous individuals are encouraged to apply.

Salary

Base salary range: $200,000 - $391,000 USD per year, depending on location, experience, and peer pay. Eligible for equity and benefits.

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