Senior Timing CAD Engineer, Applied AI at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Semiconductor, AI, TechnologyIndustries

Requirements

BS (or equivalent experience) in Electrical or Computer Engineering with 3 years of experience in AI/ML solution development, ideally for EDA, semiconductor, or complex data domains
Strong background in VLSI/ASIC design — with deep understanding of timing, constraints, STA, or sign-off workflows
Proficiency in Python, PyTorch/TensorFlow, and graph or agentic AI frameworks (e.g., LangGraph, LangChain, Ray, NetworkX)
Experience developing data pipelines, knowledge graphs, or process models for structured engineering data
Working knowledge of timing tools (PrimeTime, Nanotime, Tempus) and scripting integration with EDA environments
Experience with AI orchestration frameworks, reasoning based on prompts, and multi-agent automation is highly desirable
Strong problem-solving skills, technical depth, and a mentality for experimentation and continuous learning

Responsibilities

Architect and develop AI-driven solutions for static timing, constraints quality, and closure prediction
Integrate heterogeneous data sources — timing reports, constraint graphs, design metadata, silicon correlation — into structured knowledge bases and training pipelines
Develop autonomous analysis agents that interact with timing tools (e.g., PrimeTime, Nanotime, Tempus) to perform multi-corner, multi-mode optimization and constraint debugging
Implement scalable orchestration across Flow-Server and Digital Engineer platforms, enabling AI-in-loop decision-making for sign-off readiness
Collaborate with methodology and sign-off teams to validate models on live projects, improving coverage, predictability, and engineering productivity
Build interpretable AI pipelines using graph neural networks, large language models, and process-aware reasoning engines for timing closure recommendations
Be responsible for the end-to-end lifecycle — from data curation and model training to deployment, monitoring, and continuous improvement in production environments

Skills

Key technologies and capabilities for this role

Static Timing AnalysisPrimeTimeNanotimeTempusAIMachine LearningData PipelinesOrchestrationConstraint AnalysisASIC DesignTiming ClosurePythonAutomation Agents

Questions & Answers

Common questions about this position

What are the required qualifications for this Senior Timing CAD Engineer role?

A BS (or equivalent experience) in Electrical or Computer Engineering with 3 years of experience in AI/ML solution development, ideally for EDA, semiconductor, or complex data domains is required. Candidates need a strong background in VLSI/ASIC design with deep understanding of timing, constraints, STA, or sign-off workflows.

What technical skills and tools are essential for this position?

Proficiency in Python, PyTorch/TensorFlow, and graph or agentic AI frameworks (e.g., LangGraph, LangChain, Ray, NetworkX) is required. Experience with data pipelines, knowledge graphs, process models, and working knowledge of timing tools like PrimeTime, Nanotime, Tempus is also needed.

Is this a remote position or does it require office work?

This information is not specified in the job description.

What is the salary or compensation for this role?

This information is not specified in the job description.

What kind of experience makes a strong candidate for this Applied AI Engineer position?

Strong candidates will have 3+ years in AI/ML solution development for EDA or semiconductor domains, deep VLSI/ASIC timing expertise, proficiency in Python/PyTorch and agentic AI frameworks, plus hands-on experience with timing tools like PrimeTime and data pipelines.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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