Full-Time

Senior Deep Learning Architect

LLM Inference

Posted on 5/9/2026

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$184k - $287.5k/yr

+ Equity

Company Historically Provides H1B Sponsorship

Santa Clara, CA, USA

In Person

Category
AI & Machine Learning (1)
Required Skills
LLM
Claude
Neural Networks
Pytorch
OpenAI
Operating Systems
Requirements
  • Master's or PhD degree in Computer Science, Computer Engineering, related fields, or equivalent experience.
  • 6+ years of relevant software development experience.
  • Detailed knowledge of deep learning inference serving, PyTorch programming, profiling, and compiler optimizations.
  • Experience developing client server LLM applications with OpenAI API or MCP and identifying performance bottlenecks.
  • Solid understanding of CPU and GPU microarchitecture and performance characteristics.
  • Experience with complex software projects like frameworks, compilers, or operating systems.
  • Demonstrated proficiency with the latest AI coding agents like Claude Code, Codex, and Cursor
  • Excellent written and verbal communication skills and the ability to work independently and collaboratively in a fast-paced environment.
Responsibilities
  • Workload characterization of the latest large language models and inference servers like vLLM, SGLang and TRT-LLM to ensure NVIDIA maintains its leadership position.
  • Join forces with the performance marketing team to build engaging content, including blog posts and updates to InferenceX to highlight NVIDIA's outstanding inference achievements.
  • Collaborate with engineers from AI startup companies to establish standard benchmarking methodologies.
  • Develop a constantly evolving inference performance data results website.
  • Invent end-to-end profiling and analysis tools that you will use to keep up with the rapid pace of Generative AI.
  • Contribute to deep learning software projects, such as PyTorch, TRT-LLM, vLLM, and SGLang to drive advancements in the field.
  • Verify that new GPU product launches produce industry leading performance.
  • Collaborate across the company to guide the direction of inference serving, working with software, research, and product teams to ensure best-in-class performance.
  • Use the latest coding agents and inference technology to improve team efficiency.
Desired Qualifications
  • Demonstrate a drive to continuously improve software and hardware performance.
  • Showcase examples of novel use cases for agentic AI tools in the workplace.
  • Experience with databases and visualization tools will set you apart.

NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

Simplify Jobs

Simplify's Take

What believers are saying

  • Toyota adopts NVIDIA DRIVE AGX Orin, boosting automotive revenue 103% in Q4 FY2025.
  • SoftBank plans NVIDIA AI servers in Japan by 2030; IREN deploys 5GW infrastructure.
  • NVIDIA reaches $5.5T market cap with $216B FY revenue and $400B projected FCF.

What critics are saying

  • Broadcom supplies custom chips to Google through 2031, Anthropic from 2027, OpenAI.
  • China revenue hits zero from $17B due to US restrictions, $4.5B Q1 2026 charge.
  • B200 GPU rentals drop 30% as sentiment flips bearish, cooling FY2027 $78B guidance.

What makes NVIDIA unique

  • NVIDIA invented the GPU in 1999, pioneering accelerated computing.
  • CUDA platform from 2006 enables GPUs for AI and parallel computing.
  • Full-stack AI infrastructure powers 80% of AI training GPUs in 2025.

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Growth & Insights and Company News

Headcount

6 month growth

-1%

1 year growth

-3%

2 year growth

-2%
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