What you'd actually do

Enable and optimize state-of-the-art open models (like Nemotron and Cosmos) on NVIDIA’s accelerated inference SW stack.

Contribute new features, fix bugs and deliver production code to open-source frameworks like TRT-LLM, vLLM, SGLang, FlashInfer, etc.

Profile and analyze bottlenecks across the full inference stack to push the boundaries of inference performance.

Benchmark state-of-the-art offerings and perform competitive analysis for NVIDIA’s SW/HW stack.

Co-design with partner teams to develop the next generation of AI models and services.

Skills

Required

PhD in CS, EE or CSEE or equivalent experience
3+ years of experience
Strong background in deep learning and neural networks, in particular inference
Experience with performance profiling, analysis and optimization, especially for GPU-based applications
Proficient in PyTorch or equivalent frameworks for AI, or HPC-heavy application development
Deep understanding of computer architecture, and familiarity with the fundamentals of GPU architecture

Nice to have

Proven experience with processor and system-level performance optimization
Deep understanding of modern LLM/Diffusion architectures
Strong fundamentals in algorithms
GPU programming experience (CUDA or OpenCL)

We are looking for a Senior DL Algorithms Engineer for LLM/Omni model optimizations! Seeking senior engineers who are mindful of performance analysis and optimization to help us squeeze every last clock cycle out of Deep Learning workloads. If you are unafraid to work across all layers of the hardware/software stack from GPU architecture to Deep Learning Framework to achieve peak performance, we want to hear from you! This role offers an opportunity to directly impact the hardware and software roadmap in a fast-growing technology company that leads the AI revolution.

What you will be doing:

Enable and optimize state-of-the-art open models (like Nemotron and Cosmos) on NVIDIA’s accelerated inference SW stack.
Contribute new features, fix bugs and deliver production code to open-source frameworks like TRT-LLM, vLLM, SGLang, FlashInfer, etc.
Profile and analyze bottlenecks across the full inference stack to push the boundaries of inference performance.
Benchmark state-of-the-art offerings and perform competitive analysis for NVIDIA’s SW/HW stack.
Co-design with partner teams to develop the next generation of AI models and services.

What we want to see:

PhD in CS, EE or CSEE or equivalent experience.
3+ years of experience.
Strong background in deep learning and neural networks, in particular inference.
Experience with performance profiling, analysis and optimization, especially for GPU-based applications.
Proficient in PyTorch or equivalent frameworks for AI, or HPC-heavy application development.
Deep understanding of computer architecture, and familiarity with the fundamentals of GPU architecture.

Ways to stand out from the crowd:

Proven experience with processor and system-level performance optimization.
Deep understanding of modern LLM/Diffusion architectures.
Strong fundamentals in algorithms.
GPU programming experience (CUDA or OpenCL) is a strong plus.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May 9, 2026.

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.