NVIDIA

NVIDIA

Posted via Workday

Senior Deep Learning Performance Architect - LPU

Posted Aug 5, 2026

Role at a glance

Salary
$152K – $287.5K/yr
Location
Remote - US - California
Work arrangement
Remote
Employment
Full-time
Experience
with 5+ years of relevant experience
Education
A MS or PhD in a relevant field (CS, EE, Math) or equivalent experience

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Role Summary

AI-generated

NVIDIA is seeking a Senior Deep Learning Performance Architect to work on hardware-software co-design for AI inference performance and efficiency. The role develops performance strategies, informs future GPU architecture decisions, and collaborates with software, research, and product teams to guide AI direction.

What You'll Do

  • Design novel GPU and system architectures for AI inference performance and efficiency
  • Construct, investigate, and test deep learning algorithms and applications
  • Analyze how hardware and software architectures influence future algorithms and applications
  • Build power and performance models of the AI inference stack to guide next-generation hardware architecture
  • Collaborate with software, research, and product teams to guide the direction of AI

Generated from the employer's posting. Verify important details before applying.

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Qualifications

MS or PhD in CS, EE, Math, or equivalent experience; 5+ years of relevant experience; strong mathematical foundation in machine learning and deep learning; expert programming skills in C, C++, and/or Python; familiarity with GPU computing and HPC; strong knowledge and coursework in computer architecture.

Required

  • MS or PhD in a relevant field (CS, EE, Math) or equivalent experience
  • 5+ years of relevant experience
  • Strong mathematical foundation in machine learning and deep learning
  • Expert programming skills in C, C++, and/or Python
  • Familiarity with GPU computing (CUDA or similar) and HPC (MPI, OpenMP) stack
  • Strong knowledge and coursework in computer architecture

Preferred

  • Background with systems-level performance modeling, profiling, and analysis
  • Experience in characterizing and modeling system-level performance, accomplishing comparison studies, and documenting and publishing results
  • Background in improving AI Inference workloads by developing CUDA kernels or compilers for custom ASIC hardware

Original job description

Content provided by the employer

We are now looking for a Senior Deep Learning Performance Architect!

NVIDIA seeks a Senior DL Performance Architect to join our group of pioneers who enjoy pushing AI Inference performance boundaries. Our team focuses on ambitious hardware-software co-design to speed AI Inference workloads. This role gives an outstanding opportunity to develop world-class performance strategies, guide future GPU architecture decisions, and lead AI innovation. If you are passionate about AI efficiency Pareto curves, have a proven record of modeling LLM performance and architecting AI systems, and enjoy optimizing every cycle, this role may be perfect for you!

What you'll be doing:

  • Design novel GPU and system architectures to advance the forefront of AI Inference performance and efficiency

  • Construct, investigate, and test popular deep learning algorithms and applications

  • Understand and analyze the relationship between hardware and software architectures as it influences future algorithms and applications

  • Build efficient power and performance models of AI inference stack, while capturing minimal but significant information to guide next-gen HW architecture

  • Collaborate across the company to guide the direction of AI, working with software, research, and product teams

What we need to see:

  • A MS or PhD in a relevant field (CS, EE, Math) or equivalent experience, with 5+ years of relevant experience

  • Strong mathematical foundation in machine learning and deep learning

  • Expert programming skills in C, C++, and/or Python

  • Familiarity with GPU computing (CUDA or similar) and HPC (MPI, OpenMP) stack

  • Strong knowledge and coursework in computer architecture

Ways to stand out from the crowd:

  • Background with systems-level performance modeling, profiling, and analysis

  • Experience in characterizing and modeling system-level performance, accomplishing comparison studies, and documenting and publishing results

  • Background in improving AI Inference workloads by developing CUDA kernels or compilers for custom ASIC hardware

#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 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 August 15, 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.

NVIDIA

About the company

NVIDIA

Large Enterprise

NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.