AI for Chip Researcher

Skills

About the Role

You will define valuable chip-design problems and turn specialist knowledge into standardized methods, datasets, benchmarks, and harnesses. You will research large-model capabilities for chip design, apply AI to real development workflows, validate synthetic data, and help build data platforms, EDA toolchains, and evaluation infrastructure.

Requirements

• Background in microelectronics or integrated circuits
• Experience applying AI in practice as an engineer or project leader
• Hands-on tapeout experience across design, simulation verification, physical implementation, signoff, and tapeout
• Deep practical experience in at least one chip-design area
• Ability to define and abstract complex chip-design requirements into measurable, verifiable methods and tools
• Familiarity with at least one EDA toolchain
• Scripting and automation capability
• Experience combining chip design with large language models or agents
• Knowledge of training-data synthesis, quality evaluation, SFT/RL, design-space exploration, or tool-feedback-driven model optimization

Responsibilities

• Define high-value chip-design problems and develop standardized methods, datasets, benchmarks, and harnesses
• Convert specialist experience into chip-design and verification capabilities for foundation models
• Research large-model capabilities for design understanding, solution generation, tool use, and result analysis
• Build high-quality training data and evaluation methods
• Apply AI capabilities to real chip research and development scenarios
• Check synthetic-data correctness and validate data-synthesis results
• Contribute to chip-design data platforms, EDA toolchains, and automated evaluation infrastructure
• Participate in data cleaning and SFT/RL experiments