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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 VLM s 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 . Applications for this job will be accepted at least until August 21, 2026. 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.