Research Engineer, AI for Chip Design

Job Description

Job Description

Full-time · On-site · San Jose, CA · Austin, TX or Taiwan

About Our Client

Our client is building the next generation of design automation for the semiconductor industry.

Our mission is to enable every engineering organization to build its own self-improving agentic design workforce. Its product combines AI agents, engineering knowledge, agent-native tools, advanced models, and continuous learning to automate complex chip-design workflows.

Our team brings deep experience in artificial intelligence, electronic design automation, semiconductor design, GPU-accelerated computing, and production software systems. We work closely with leading semiconductor companies to turn advanced research into technology that improves engineering productivity, design quality, and time to market.

The Role

We are looking for an exceptional Research Engineer to develop new technologies at the intersection of artificial intelligence, agentic systems, GPU-accelerated computing, and Electronic Design Automation.

You will identify important research problems, develop novel algorithms and agent-native tools, build working prototypes, and help deploy them in real semiconductor design environments. Your work may span AI agents, large language models, reinforcement learning, optimization, GPU-accelerated algorithms, verification, analog design, and other areas of chip design automation.

This role is ideal for someone who combines strong research ability with exceptional implementation skills and wants to see their ideas used in production—not remain only in papers or prototypes.

What You'll Do

• Develop new AI and agentic methods for semiconductor design and verification.

• Build novel agent-native tools and algorithms designed specifically for autonomous engineering workflows, rather than adapting interfaces built primarily for human users.

• Develop GPU-accelerated algorithms for computationally intensive design, analysis, search, simulation, and optimization problems.

• Create tools that expose design state, constraints, actions, feedback, and optimization objectives in forms that agents can reason over and use effectively.

• Build agents that can understand engineering objectives, use EDA tools, execute multi-step workflows, analyze results, recover from failures, and improve over time.

• Research and implement techniques involving large language models, reinforcement learning, parallel algorithms, search, optimization, program synthesis, and machine learning for engineering systems.

• Develop solutions for workflows such as functional verification, analog and custom design, RTL development, synthesis, timing analysis, and physical design.

• Design rigorous evaluation methods for engineering agents, including problems where design data is private, sparse, or customer-specific.

• Translate promising research ideas into reliable, scalable product capabilities.

• Integrate AI systems with simulators, formal tools, design databases, commercial EDA tools, GPU computing platforms, and customer engineering infrastructure.

• Work directly with semiconductor engineers to understand complex workflows and identify high-impact automation opportunities.

• Collaborate with research, product, platform, and solutions teams across San Jose, Austin, and Taiwan.

• Contribute to patents, publications, technical presentations, and the broader development of Agentic Design Automation.

What We're Looking For

• PhD or master's degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field, or equivalent practical experience.

• Strong programming skills in Python and proficiency in at least one systems language such as C++ or Rust.

• Experience with machine learning frameworks such as PyTorch or JAX.

• Demonstrated research or engineering experience in one or more of the following:

• Electronic Design Automation

• Semiconductor design or verification

• Agentic AI or large language models

• GPU-accelerated or parallel algorithms

• Reinforcement learning

• Combinatorial optimization

• Program synthesis or code generation

• Formal methods

• Machine learning for engineering or scientific applications

• Ability to take an ambiguous technical problem from initial formulation through experimentation, implementation, and evaluation.

• Strong analytical, software engineering, optimization, and debugging skills.

• High ownership, intellectual curiosity, and willingness to work across research and product boundaries.

• Clear written and verbal communication skills.

Particularly Valuable Experience

• Publications in leading EDA, AI, machine learning, systems, high-performance computing, or computer architecture venues.

• Experience developing new EDA algorithms, optimization engines, design representations, or domain-specific tools.

• Experience developing GPU-accelerated algorithms using CUDA, Triton, or related parallel-computing technologies.

• Experience profiling and optimizing computational workloads across CPUs and GPUs.

• Experience designing tools or environments for use by autonomous agents.

• Experience with simulation, verification, synthesis, timing analysis, physical design, analog design, or layout.

• Experience building agents that interact with tools, codebases, databases, or external environments.

• Experience with LLM training, post-training, fine-tuning, retrieval, tool use, or evaluation.

• Familiarity with Verilog, SystemVerilog, assertions, SPICE, TCL, or semiconductor design flows.

• Experience with commercial EDA tools or production chip-design environments.

• Experience deploying AI systems in enterprise or security-sensitive environments.

• A strong record of implementation through research systems, open-source projects, production software, or technical competitions.

Why Our Client

You will have the opportunity to:

• Help define a new category of semiconductor design technology.

• Invent the agent-native algorithms and tools that will form the foundation of future automated design workflows.

• Develop GPU-accelerated algorithms that make previously impractical design and optimization workflows possible.

• Build AI systems that perform complex, consequential engineering work—not just generate recommendations.

• Work with real semiconductor workflows, tools, and private engineering knowledge.

• See your research deployed directly with leading chip-design organizations.

• Work in a small, highly technical team where individual contributions can shape the product and company.

• Collaborate with colleagues across San Jose, Austin, and Taiwan.

• Change how chips are designed, rather than focus on only one design or one point tool.