10 days ago
Base Salary
$272k - $431k/yr
Responsibilities
- Define and prototype AI system architectures for optical and e-beam inspection, wafer and mask inspection, metrology, defect review, equipment signals, and process data.
- Develop computer vision, time-series, multimodal, anomaly-detection, model-adaptation, defect-analysis, and production-inference capabilities.
- Create evaluation, adaptation, and data-sovereignty workflows that measure robustness across tools, fabs, and shifting production distributions.
- Connect inspection and metrology results with equipment signals, process history, wafer genealogy, SPC, and yield outcomes for root-cause analysis.
- Design secure agentic workflows for air-gapped fabs covering data triage, inference, review assistance, human approval, and deployment.
- Optimize and package inference for production latency, memory, throughput, reliability, and security requirements.
- Work with fabs, equipment manufacturers, and cross-functional research, software, process, metrology, inspection, and hardware teams to move prototypes into partner-operated production and define the technical roadmap.
Requirements
- MS or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent experience.
- 12+ years of professional software or algorithm engineering experience with demonstrated technical ownership and architectural direction; directly relevant PhD research may count toward this total.
- 8+ years of experience in semiconductor manufacturing, including inspection, metrology, defect review, process control, yield engineering, or fab-data systems.
- Hands-on delivery of software shipped in semiconductor equipment, operated in a fab, or adopted by a manufacturing customer.
- 4+ years of hands-on deep learning, machine learning, computer vision, or applied AI experience, including work within the last 24 months.
- Strong Python skills and production experience with PyTorch or TensorFlow.
- Working GPU literacy, experience reasoning about inference performance and hardware or software bottlenecks, and a record of taking ambiguous problems through architecture, implementation, evaluation, and validated results.
- Direct engagement with fabs, manufacturing teams, or equipment vendors on technical requirements.
- Preferred experience includes adapting pretrained vision, multimodal, or world foundation models; anomaly detection; time-series modeling; FDC; virtual metrology; advanced-packaging inspection; sparse-data evaluation; synthetic data; self-supervised or few-shot learning; domain adaptation; model compression; inference optimization; SDKs; platform capabilities; or agentic workflows.
Benefits
- Competitive salary, equity, and a generous benefits package.
- Position based in Santa Clara.
- Applications accepted at least until November 3, 2026; the posting represents an existing vacancy.
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.