We’re forming a team of innovators to roll out and enhance AI inference solutions at scale, demonstrating NVIDIA’s GPU technology and Kubernetes. As a Solutions Architect focused on inference, you’ll collaborate closely with our engineering, DevOps, and customers to develop enterprise AI solutions. Together, we'll deliver generative AI to production!
What you'll be doing:
Build inference pipelines with tools like NVIDIA Dynamo, distributing tasks among GPU workers to improve efficiency.
Collaborate with DevOps teams to orchestrate disaggregated inference using Kubernetes for complex workloads.
Accelerate inference pipelines using TensorRT-LLM, vLLM, SGLang, and other backends to ensure seamless integration with disaggregated inference.
Provide mentorship and technical leadership to customers and internal teams, guiding them through the deployment of disaggregated inference systems and resolving complex issues.
What we need to see:
5+ Years in Solutions Architecture with a proven track record of deploying distributed systems and AI inference workloads on Kubernetes.
Experience with one of NVIDIA Dynamo, Triton Inference Server, or TensorRT-LLM for model optimization and serving.
GPU orchestration using NVIDIA GPU Operator, NIM Operator, and Multi-Instance GPU (MIG) partitioning.
Solving sophisticated GPU allocation, memory hierarchies (HBM, DRAM, SSD), and low-latency networking (RDMA, UCX).
Demonstrated success in tuning large language models for low-latency inference in enterprise environments.
BS in CS/Engineering or equivalent experience.
Ways to stand out from the crowd:
Prior experience deploying NVIDIA inference technologies such as Dynamo, NIM, NIXL and Grove.
Deep understanding of transformer neural network, and inference acceleration technologies like quantization, speculative decoding, WideEP etc.
NVIDIA Certified AI Engineer or similar credentials.
Contributions to open-source projects including NVIDIA Dynamo, vLLM, KServe, or SGLang.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- 5+ Years in Solutions Architecture
- Experience deploying distributed systems and AI inference workloads on Kubernetes
- Experience with NVIDIA Dynamo, Triton Inference Server, or TensorRT-LLM
- BS in CS/Engineering or equivalent experience
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.”
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