NVIDIA is looking for a senior technical lead to drive infrastructure and tooling development for our Automation team. This role will focus on building scalable internal platforms, automation frameworks, developer productivity tools, and LLM-powered workflows that improve engineering efficiency across complex software development and validation environments.
What You’ll Be Doing:
Lead the design and development of infrastructure, automation frameworks, and internal engineering tools.
Build scalable services, APIs, dashboards, workflow engines, and integrations that improve developer efficiency and operational visibility.
Develop LLM-based workflows for triage, summarization, code and log analysis, test workflow assistance, report generation, and knowledge retrieval.
Integrate tooling with CI/CD systems, source control, issue tracking, test infrastructure, dashboards, and internal engineering services.
Define architecture, coding standards, evaluation methods, and reliability practices for automation and LLM-enabled systems.
Mentor engineers, review designs, and provide technical leadership across infrastructure and tooling projects.
What We Need To See:
BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
8+ years of software engineering experience, with strong hands-on development skills.
Proven experience building infrastructure, automation systems, developer tools, workflow platforms, or internal engineering services.
Strong programming experience in Python, Bash, C, and C++, with experience building infrastructure, automation, and systems-level tooling in Linux-based environments.
Experience designing systems that integrate with CI/CD pipelines, source control systems, issue trackers, databases, APIs, and distributed services.
Hands-on experience developing LLM-based workflows, agents, RAG systems, timely pipelines, or AI-assisted automation tools.
Practical understanding of LLM workflow reliability, including evaluation, guardrails, error handling, observability, and human-in-the-loop review.
Strong technical leadership, architecture ownership, mentoring, and cross-team collaboration skills.
Ways To Stand Out From The Crowd:
Experience building engineering efficiency platforms or automation infrastructure for large-scale software organizations.
Experience with test automation, validation infrastructure, build systems, release workflows, or developer experience tooling.
Familiarity with embeddings, vector search, RAG, model evaluation, agent orchestration, or LLM workflow frameworks.
Strong background in Linux, containers, Kubernetes, cloud or on-prem infrastructure, and distributed systems.
Prior experience leading a small technical team or serving as a technical lead for multi-functional infrastructure projects.
You will also be eligible for equity and benefits.
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.Skills Required
- BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
- 8+ years of software engineering experience with strong hands-on development skills.
- Proven experience building infrastructure, automation systems, developer tools, workflow platforms, or internal engineering services.
- Strong programming experience in Python, Bash, C, and C++.
- Experience building infrastructure, automation, and systems-level tooling in Linux-based environments.
- Experience designing systems that integrate with CI/CD pipelines, source control systems, issue trackers, databases, APIs, and distributed services.
- Hands-on experience developing LLM-based workflows, agents, RAG systems, pipelines, or AI-assisted automation tools.
- Practical understanding of LLM workflow reliability, including evaluation, guardrails, error handling, observability, and human-in-the-loop review.
- Strong technical leadership, architecture ownership, mentoring, and cross-team collaboration skills.
- Experience building engineering efficiency platforms, test automation, validation infrastructure, build systems, release workflows, or developer experience tooling.
- Familiarity with embeddings, vector search, model evaluation, or agent orchestration frameworks.
- Background in Linux, containers, Kubernetes, cloud or on-prem infrastructure, and distributed systems.
- Prior experience leading a small technical team or serving as a technical lead for multi-functional infrastructure projects.
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.
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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.
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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.
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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.
NVIDIA Insights
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.”
