Staff Silicon Engineer

About the role

We are seeking a Staff Silicon Engineer who can combine strong software engineering, applied AI/ML, and semiconductor-domain understanding to build production-grade tools for chip design and verification.

This work matters because chip design is becoming too complex for traditional workflows alone, and AI-native engineering systems can meaningfully change the speed, quality, and scale of semiconductor development.

Key Responsibilities

• Bridge Hardware and Software: Translate deep semiconductor domain expertise into robust software requirements and implementations.
• Develop Agentic Data Pipelines: Architect and implement agentic workflows specifically designed to generate synthetic or augmented RTL and UVM code, creating essential datasets for training and fine-tuning advanced AI models.
• Agentic Workflow Optimization: Fine-tune and optimize agentic workflow for domain-specific engineering tasks such as RTL generation, verification planning, and bug triage.
• Infrastructure Development: Build and maintain high-performance simulation and testing infrastructure to validate AI-generated hardware designs.
• Technical Leadership: Provide mentorship to junior engineers and lead cross-functional projects involving AI researchers and hardware architects.

Required Qualifications

• Experience: 8+ years of experience in silicon design, verification, or hardware-focused software development.
• Semiconductor Domain: Deep understanding of the ASIC/FPGA design lifecycle in at least two of the three phases: RTL design (Verilog/SystemVerilog), UVM-based verification, and physical design flows.
• EDA Tools: Experience with, and being a power user of, commercial EDA tools from major vendors (Cadence, Synopsys, Mentor/Siemens).
• Software Proficiency: Strong programming skills in Python, C++, or similar languages, with experience in building scalable software systems.
• AI/ML Knowledge: Practical experience with LLMs, prompt engineering, or machine learning frameworks (PyTorch/TensorFlow) applied to technical domains.
• Education: MS or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field.

Preferred Qualifications

• Experience with LangSmith and LangGraph to build Agentic workflow.
• Background in computer architecture, specifically for AI accelerators or high-performance computing.
• Contributions to open-source hardware or AI projects.