NVIDIA’s accelerated computing platform is enabling the generational improvements in large language models, while the scale and complexity of these models are creating new challenges in computational efficiency. We are seeking a strong technical leader to drive a unified strategy for making LLMs more efficient from research through deployment. This role will bring together model innovation, systems expertise, and hardware awareness to ensure that new capabilities can be delivered within practical constraints of compute, memory, power, and cost. You will lead a multidisciplinary effort, establish the technical direction for LLM efficiency, and help shape how future models and computing platforms are designed together. The ideal candidate is a hands-on engineer who enjoys finding fundamental bottlenecks, challenging conventional boundaries between disciplines, and turning research ideas into scalable, real-world improvements.
What you will be doing:
Lead cross-layer efforts to improve the efficiency of large language models across model architecture, training and inference systems.
Analyze how LLM workloads map to GPUs, memory systems, interconnects, and distributed infrastructure, and identify opportunities for model-system-hardware co-design.
Establish a measurement-driven efficiency roadmap and lead projects from early investigation through production deployment.
Partner with model researchers, systems engineers, compiler and kernel developers, and hardware architects to influence future model, software, and hardware roadmaps.
What we need to see:
MS or PhD degree, or equivalent experience, in Computer Science, Electrical Engineering, Computer Engineering, or a related field.
5+ years of relevant experience in AI systems, model architecture, computer architecture, high-performance computing, or performance optimization.
Strong understanding of LLM architectures, training and inference workloads, and the tradeoffs between model quality, computational cost, memory footprint, latency, throughput, and power.
Strong background in performance analysis, roofline modeling, workload characterization, benchmarking, and hardware-aware optimization.
Proven ability to provide technical leadership and drive complex optimization projects from concept to production.
Ways to Stand Out from the Crowd:
A track record of delivering measurable improvement throughput, cost per token, energy per token, memory efficiency, or time to train.
A first-principles - measure, model, optimize, and deliver - approach to improving LLM efficiency.
Familiarity with low-precision computation, quantization, sparsity, Mixture-of-Experts, long-context inference, and speculative decoding.
Experience co-designing model architectures with training, inference, compiler, or hardware constraints.
Experience influencing accelerator, system, or datacenter architecture based on future AI workload requirements.
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. Are you creative architect interested in pushing silicon to its highest performance and efficiency ? If so, we want to hear from you! Come, join our DL Architecture team and help build the real-time, cost-effective AI computing platform driving our success in this exciting and quickly growing field.
#LI-Hybrid
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
- MS or PhD degree, or equivalent experience, in Computer Science, Electrical Engineering, Computer Engineering, or a related field
- 5+ years of relevant experience in AI systems, model architecture, computer architecture, high-performance computing, or performance optimization
- Strong understanding of LLM architectures, training and inference workloads, and tradeoffs involving model quality, computational cost, memory footprint, latency, throughput, and power
- Strong background in performance analysis, roofline modeling, workload characterization, benchmarking, and hardware-aware optimization
- Ability to provide technical leadership and drive complex optimization projects from concept to production
- Track record of delivering measurable improvements in throughput, cost per token, energy per token, memory efficiency, or time to train
- First-principles approach to improving LLM efficiency through measurement, modeling, optimization, and delivery
- Familiarity with low-precision computation, quantization, sparsity, Mixture-of-Experts, long-context inference, and speculative decoding
- Experience co-designing model architectures with training, inference, compiler, or hardware constraints
- Experience influencing accelerator, system, or datacenter architecture based on future AI workload requirements
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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