Senior Applied AI Engineer, Manufacturing & System Co-Design

Job Title

Senior Applied AI Engineer, Manufacturing & System Co-Design

Role Summary

Lead methodology and infrastructure that ensure alignment between system product-of-record (POR) specifications and manufacturing test specifications across NVIDIA GPU, SoC, and CPU programs. The role sits on the System–Manufacturing Architecture (SMAC) team and focuses on building workflows, checks, and automation to surface specification drift before it reaches silicon.

This is hands-on work: design workflow architecture, implement production-grade pipelines and CI, and drive cross-organizational adoption so the tooling becomes relied-upon infrastructure.

Experience Level

Senior — typical candidate has 8+ years of experience in system software, silicon bring-up, or productization engineering.

Responsibilities

Own methodology, tooling, and integrations that keep system intent aligned with manufacturing reality across programs and generations.

• Define manufacturing specification types, schemas, and semantics derived from system PORs; govern versioning and validation across the program lifecycle.
• Design and implement production-grade Python pipelines and automated checks to detect spec drift across ATE, SLT, BLT, L10+, and tester workflows.
• Integrate SMAC work into program milestones, gates, and artifacts; define TPM-driven attestation when checks lag.
• Build agent-ready tooling and CI: CLIs, MCPs, retrieval of bugs/specs, human-in-the-loop checkpoints, and evaluation-based CI gates against real workflows.
• Drive cross-org adoption with Design, Operations, Post-Silicon, and DFX teams; convert proofs-of-concept into production infrastructure.

Requirements

Core technical skills and demonstrated impact required; education details are summarized separately below.

• Strong Python development experience; track record shipping production services and data pipelines.
• Deep systems skills and practical experience with silicon bring-up, test flows, or productization pipelines.
• Thorough understanding of spec ecosystems: guard-bands, manufacturing screen specs, test insertion constraints, and how drift manifests.
• Proven cross-organizational influence: methodologies or workflows you've designed that others adopted.
• Experience building CI/automation and agent-ready tooling that integrates with engineering workflows and evaluation gates.
• Ability to interpret silicon/productization outputs (speed, power, binning) and apply applied-AI with reviewable artifacts and clear judgment on manual validation.

Nice-to-have:

• Experience deploying LLM-backed tools that are relied upon by subject-matter experts.
• History of standing up cross-org workflows that survived adoption across resistant partners.

Education Requirements

BS or MS in Electrical Engineering, Computer Engineering, Computer Science, or Systems Engineering — or equivalent practical experience. (Source explicitly lists "BS, MS, or equivalent experience" in these fields.)

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.