NVIDIA

2 months ago

Applied AI Engineer, Product Convergence and Closure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Build AI-driven pipelines that turn raw silicon simulation data into firmware tuning, product specifications, and manufacturing limits. Own the integration between toolchains so simulation outputs flow reliably into downstream systems. Use LLMs and agents to automate engineering analysis, validation, and reporting, and create observability/validation to catch data errors before release. Collaborate with silicon architecture, firmware, and manufacturing teams to translate new hardware requirements into production workflows.

Location: Shanghai

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Build infrastructure and pipelines that convert simulation data (power, noise, binning yields, and more) into firmware tuning, product specs, and manufacturing limits.
•Use LLMs and agents across the toolchain to automate analysis, validation, and reporting currently done manually.
•Create observability and validation systems to detect data errors and inconsistencies before they become release blockers.
•Partner with product convergence, silicon architecture, firmware, and manufacturing teams to translate new hardware requirements into production workflows.

Key Requirements

•BS/MS in CS, CE, EE, or Systems Engineering, or equivalent experience.
•4+ years shipping production Python services and data pipelines (FastAPI, async workflows, databases, modern web frontends).
•Hands-on experience applying LLMs to engineering problems (agents, MCP, RAG, or evaluation pipelines), including shipping an LLM-backed production feature.
•Strong instincts for data quality, including automated checks, schema validation, and integration tests for changing inputs.
•Keep up with fast-paced AI developments and distinguish impactful tools from hype.

Experience:4+ years

Education:

Tech Stack:PythonFastAPIAsyncDatabasesWeb frontendsLLMsAgentsMCPRAGEvaluation pipelinesDSPyPerlDashboardsVisualizations

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

2 months ago

Applied AI Engineer, Product Convergence and Closure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: Shanghai

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Build infrastructure and pipelines that convert simulation data (power, noise, binning yields, and more) into firmware tuning, product specs, and manufacturing limits.
•Use LLMs and agents across the toolchain to automate analysis, validation, and reporting currently done manually.
•Create observability and validation systems to detect data errors and inconsistencies before they become release blockers.
•Partner with product convergence, silicon architecture, firmware, and manufacturing teams to translate new hardware requirements into production workflows.

Key Requirements

•BS/MS in CS, CE, EE, or Systems Engineering, or equivalent experience.
•4+ years shipping production Python services and data pipelines (FastAPI, async workflows, databases, modern web frontends).
•Hands-on experience applying LLMs to engineering problems (agents, MCP, RAG, or evaluation pipelines), including shipping an LLM-backed production feature.
•Strong instincts for data quality, including automated checks, schema validation, and integration tests for changing inputs.
•Keep up with fast-paced AI developments and distinguish impactful tools from hype.

Experience:4+ years

Education:

Tech Stack:PythonFastAPIAsyncDatabasesWeb frontendsLLMsAgentsMCPRAGEvaluation pipelinesDSPyPerlDashboardsVisualizations

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor