Senior System Software Engineer - Local AI

Posted 3 Days Ago
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Pune, Maharashtra, IND
In-Office
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop system software and infrastructure for deploying, benchmarking, and qualifying local AI workloads across Linux and Windows GPU systems. Build CI pipelines, model management services, databases, data pipelines, dashboards, telemetry, scheduling systems, and autonomous infrastructure agents. Collaborate with inference and application teams to improve reproducibility, diagnose system issues, detect regressions, and optimize performance.
Summary Generated by Built In

NVIDIA has continuously reinvented itself for over two decades. The invention of the GPU in 1999 fueled the growth of PC gaming, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning has ignited modern AI, positioning NVIDIA as a leading AI computing company.


Artificial intelligence is shifting from passive assistance to autonomous, always-on workflows. Our mission is to make this transition seamless, efficient, and secure for millions globally. The Local AI Inference team is seeking a Senior System Software Engineer to lead the development of robust system-level software and infrastructure for deploying, benchmarking, and qualifying high-quality AI applications and models across Linux and Windows systems powered by RTX and DGX-class GPUs. You will partner with inference engineers, model developers, and AI application teams to build reliable, scalable infrastructure that enables reproducible evaluation and improves local AI performance. Using AI tools and developing autonomous agents, you will accelerate infrastructure development, automate operations, and help engineers diagnose complex system issues. If you enjoy tackling systems challenges and shaping how AI infrastructure is built and operated, we would love to hear from you!


What you'll be doing:

  • Partner across organizations with benchmarking teams, inference teams, and AI application developers to define requirements, design, and maintain shared infrastructure for deploying, benchmarking, and qualifying AI workloads across infernece backends such as llama.cpp, Ollama, PyTorch, vLLM, and WinML.
  • Build Git-based CI infrastructure for automated builds, integration testing, and regression detection across hardware platforms, operating systems, and runtime configurations.
  • Develop model management services to download, version, cache, and synchronize checkpoints and dependencies for reproducible execution.
  • Design databases, backend services, and data pipelines to store and query model metadata, benchmark results, system telemetry, and execution artifacts.
  • Create dashboards and reports that help engineers compare inference configurations, identify regressions, and investigate performance bottlenecks.
  • Develop and operate server infrastructure, including environment provisioning, workload scheduling, resource management, and failure recovery.
  • Develop autonomous AI agents for infrastructure management, workload orchestration, monitoring, and diagnostics, with appropriate access controls and execution safeguards.
  • Maintain Git repositories and companion websites, keeping code, documentation, model recommendations, and evaluation results current.
  • Collaborate with inference developers to implement instrumentation and debugging capabilities, resolve system issues, and improve infrastructure efficiency and scalability.

What we need to see:

  • B.Tech or higher degree in Computer Science, Computer Engineering, Information Technology, Software Engineering, or a related field, or equivalent experience.
  • 5+ years of relevant software development, with strong programming skills in Python, C/C++, C#, or another systems programming language.
  • Experience developing system software, backend services, or infrastructure supporting engineering development.
  • Understanding of system APIs, multithreading, process and resource management, and systems debugging.
  • Hands-on with Git-based CI, databases, SQL, and data modeling.
  • Strong analytical and problem-solving skills, with the ability to independently own projects and manage competing priorities.
  • Excellent written and verbal communication skills for collaboration across engineering teams.

Ways to stand out from the crowd:

  • Track record of integrating or debugging AI inference frameworks and building model evaluation or benchmarking platforms
  • Proficiency working with AI agents, including tool integration and evaluation of autonomous infrastructure workflows.
  • Hands-on expertise in developing telemetry and observability systems using tools such as Grafana or Kibana.
  • Demonstrated ability to publish technical documentation, model recommendations, or benchmark results through companion websites.

We're a top employer known for innovation and growth. We are an equal-opportunity employer and value diversity at our company. With competitive salaries and a generous benefits package, we are widely considered to be one of the world’s most desirable employers of technology. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we would like to hear from you. 

Skills Required

  • B.Tech or higher in Computer Science, Computer Engineering, Information Technology, Software Engineering, a related field, or equivalent experience
  • 5+ years of relevant software development experience
  • Strong programming skills in Python, C/C++, C#, or another systems programming language
  • Experience developing system software, backend services, or infrastructure supporting engineering development
  • Understanding of system APIs, multithreading, process and resource management, and systems debugging
  • Hands-on experience with Git-based CI, databases, SQL, and data modeling
  • Strong analytical and problem-solving skills, with the ability to independently own projects and manage competing priorities
  • Excellent written and verbal communication skills for collaboration across engineering teams
  • Experience integrating or debugging AI inference frameworks and building model evaluation or benchmarking platforms
  • Proficiency working with AI agents, including tool integration and evaluation of autonomous infrastructure workflows
  • Experience developing telemetry and observability systems using Grafana or Kibana
  • Ability to publish technical documentation, model recommendations, or benchmark results through companion websites

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.

  • 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.
  • 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.
  • 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.

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

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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