NVIDIA has transformed accelerated computing through innovation powered by exceptional technology and people. Within ASIC networking product engineering group, you will help bring AI into product engineering by turning fragmented engineering data into scalable, production-ready solutions for analysis, decision-making, and efficiency.
In this role, you will define and deliver AI solutions that unify data across NVIDIA infrastructure and engineering systems, enabling advanced analytics for production engineering teams through AI agents, copilots, and workflow automation. You will own solutions end to end, from architecture and development through deployment, maintenance, and continuous improvement, and help shape how ASIC networking product engineering uses AI to scale engineering productivity.
What you'll be doing:
Design, build, and maintain AI solutions that improve our division efficiency across production, characterization, analysis, and operational workflows.
Develop agentic analytics capabilities that enable engineers to query, analyze, and reason over ASIC data using AI agents and copilots.
Consolidate data from multiple infrastructure and engineering systems into scalable, reliable pipelines and reusable services.
Partner with production engineering teams to identify pain points, define high-value use cases, and deliver measurable impact.
Build and support tools for data access, automation, reporting, anomaly detection, and engineering insight generation.
Collaborate across NVIDIA to align interfaces, improve data quality, and support scalable deployment models.
Drive continuous improvement through user feedback, monitoring, and roadmap planning.
What we need to see:
Bachelor’s in Computer Science, Software Engineering, Data Science, or a related field, or equivalent experience.
8+ years of experience as an AI solutions engineer, machine learning engineer, or software engineer building production AI/data solutions.
Strong experience designing, developing, deploying, and maintaining end-to-end AI applications in production.
Hands-on expertise with Python and modern software engineering practices.
Practical experience with LLMs, AI agents, RAG, workflow orchestration, and data/analytics applications.
Strong background building data pipelines, APIs, services, and applications on top of structured and semi-structured engineering data.
Strong communication skills and a proactive, ownership-driven mindset.
Advantage: experience in semiconductor, hardware, product engineering, test, characterization, or manufacturing analytics environments.
Ways to stand out from the crowd:
Experience building AI solutions for engineering or manufacturing organizations.
Familiarity with agent frameworks, vector databases, telemetry platforms, or internal knowledge/data systems.
Background in cross-functional work spanning software, data, infrastructure, and product engineering.
Proven track record of introducing new technical capabilities and driving adoption across engineering teams.
Skills Required
- Bachelor's in Computer Science, Software Engineering, Data Science, or related field (or equivalent experience).
- 8+ years of experience as an AI solutions engineer, machine learning engineer, or software engineer building production AI/data solutions.
- Strong experience designing, developing, deploying, and maintaining end-to-end AI applications in production.
- Hands-on expertise with Python and modern software engineering practices.
- Practical experience with LLMs, AI agents, RAG, workflow orchestration, and data/analytics applications.
- Strong background building data pipelines, APIs, services, and applications on structured and semi-structured engineering data.
- Strong communication skills and a proactive, ownership-driven mindset.
- Experience in semiconductor, hardware, product engineering, test, characterization, or manufacturing analytics environments.
- Familiarity with agent frameworks, vector databases, or telemetry platforms and internal knowledge/data systems.
- Background in cross-functional work spanning software, data, infrastructure, and product engineering.
- Proven track record of introducing new technical capabilities and driving adoption across engineering teams.
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.”
