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Senior Software Architect

NVIDIA

Austin, TXOn Site

Original Job Summary

About the Role

The Senior Software Architect at NVIDIA will redefine engineering flows using generative AI to build agentic systems, improve chip designs, and enhance AI-powered workflows. This role supports the hardware architecture team to employ AI in designing next-generation AI systems.

What You’ll Be Doing

  • Implement and deploy AI applications based on large language models, agentic frameworks, and custom models.
  • Collaborate with hardware architects to design and deploy AI solutions tailored to specific problems.
  • Work with infrastructure engineers to enhance existing automated workflows with LLMs.
  • Develop and optimize retrieval and generation algorithms for enterprise data including text, code, and images.
  • Engage with internal research teams to address complex chip design challenges using ML and DL.
  • Research emerging AI technologies and engineering practices to maintain a competitive edge.

What We Need To See

  • MSc or PhD in a related field or equivalent experience.
  • Over 5 years of industry or research experience.
  • Deep expertise in LLMs, ML/DL, and agent development.
  • Experience in training/fine-tuning models, multi-agent systems, RAG pipelines, and vector databases.
  • Strong analytical, communication, and interpersonal skills.

Ways To Stand Out

  • Experience in computer architecture or hardware development.
  • Understanding of distributed systems and microservice architecture.
  • Hands-on experience with NVIDIA Inference Microservices (NIMs).

Key Skills/Competency

  • Generative AI
  • Agentic Systems
  • LLMs
  • Machine Learning
  • Deep Learning
  • Hardware Architecture
  • Automated Workflows
  • Data Algorithms
  • Distributed Systems
  • Research & Development

How to Get Hired at NVIDIA

🎯 Tips for Getting Hired

  • Customize your resume: Highlight AI and architecture experience.
  • Research NVIDIA: Understand their innovations in accelerated computing.
  • Prepare for technical interviews: Review agentic and LLM frameworks.
  • Demonstrate problem-solving: Share real-world AI solution examples.

📝 Interview Preparation Advice

Technical Preparation

Review LLM frameworks and agentic systems.
Study machine learning and deep learning case studies.
Prepare code samples for algorithm optimization.
Analyze automated workflow integration techniques.

Behavioral Questions

Describe a complex project challenge faced.
Explain your collaboration experience with diverse teams.
Share a time you optimized a difficult process.
Discuss handling feedback in high-pressure situations.