NVIDIA is looking for an Engineering Manager to lead the team responsible for deploying and serving Large Language Models (LLMs) and Vision-Language Models (VLMs) at scale.
Our team builds and operates an AI inference platform that enables customers to deploy and run brand new generative AI models efficiently across NVIDIA GPU platforms. The platform operates at the intersection of model optimization, inference systems, distributed computing, and production infrastructure. In this role, you will manage a team of engineers responsible for taking modern models from research and experimentation into highly optimized, reliable, and scalable production deployments. You will work closely with research scientists, software engineers, and hardware specialists to push the boundaries of AI inference performance and deliver a world-class model deployment platform.
What you will be doing:
Lead, mentor, and grow a high-performing team building and operating a platform for deploying GenAI models at scale.
Drive the deployment and optimization of LLMs and VLMs for low-latency, high-throughput, and cost-efficient inference.
Analyze, profile, and optimize end-to-end deep learning workloads across the model, inference stack, distributed systems, and GPU hardware.
Work closely with research teams and model developers to bring new architectures and models from prototype to production.
Drive technical strategy and roadmap for model deployment, inference optimization, scalability, reliability, and performance.
Establish engineering standards for benchmarking, profiling, production deployment, and continuous performance optimization.
Collaborate with internal and external partners to enable seamless deployment of rapidly evolving GenAI models.
.
What we want to see:
Master’s or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience.
8+ years overall experience, including 3 years of management/leadership.
Strong hands-on experience with LLMs and/or VLMs and a solid understanding of modern deep learning architectures.
Deep expertise in inference optimization techniques such as quantization, speculative decoding, continuous batching, prefix caching, and KV-cache optimization.
Experience with disaggregated inference/serving, distributed inference, multi-node deployments, and GPU cluster orchestration.
Strong technical leadership, communication, and people-management skills.
Ways to stand out from the crowd:
Experience building or operating AI inference, model serving, or model deployment platforms.
Practical experience in working with TensorRT, TensorRT-LLM, vLLM, SGLang, or comparable inference/serving frameworks.
Experience building highly available, scalable, and observable production services for AI workloads.
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!
#LI-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 3, and 272,000 USD - 431,250 USD for Level 4.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 an inclusive 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
- Minimum BSc or equivalent experience
- 8+ years of related overall experience
- 3+ years of management/leadership experience
- Experience leading multiple software engineering projects
- Strong experience with Large Language Models (LLMs) and Large Visual-Language Models (VLMs)
- Excellent programming, debugging, performance analysis, and test design skills
- Great communication and interpersonal skills
- Ability to work in a multifaceted, product-centric environment
- Experience with inference of deep learning models
- Experience doing performance analysis and tuning
- Exposure to inference platforms (TensorRT-LLM, vLLM, SGLang)
- Familiarity with project management tools (JIRA, Microsoft Project)
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.
-
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.
-
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.
-
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.”
