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Cognichip Tech Stack
AI-powered chip design platform using physics-informed foundation models
Cognichip applies physics-informed AI to semiconductor design, with a tech stack spanning EDA tools (Vivado, Quartus, Verilator), hardware description languages (SystemVerilog, Verilog), AI frameworks (PyTorch, TensorFlow, LangChain, LangGraph), and formal verification (Z3, Coq). The hiring mix is heavily engineering-skewed (37 of 43 open roles) with 17 staff-level engineers, signaling deep technical scaling around agentic workflows and synthetic data generation for logic verification—core projects that address a stated pain point of low semiconductor design productivity.
Tech Stack 61 technologies
Core StackAWS Kubernetes Java Python Go TypeScript LangChain C++ Rust PyTorch TensorFlow Docker IAM SystemVerilog UVM LangGraph AutoGen CrewAI Vivado Vitis Quartus Verilator CocoTB DDR5 FPGA Verilog Coq Z3 Tcl Bash+31 more
What Cognichip Is Building
◆Challenges
- Scaling chip design workflows
- Adopting ai in silicon design
- Meeting soc 2 compliance
- Integration with simulation engines
- Low semiconductor design productivity
- Optimizing agent performance
- Deploying agentic ai workflows
- Building security function
- Technical debt reduction
- Hallucination detection in ai-generated hardware
▲Active Projects
- Novel chip design methodologies for ai optimization
- Ai-enabled silicon design and verification workflows
- Playbooks for eda tool usage
- Synthetic data engineering for logic verification
- Build benchmarks and reference designs
- Generate massive datasets for ai training
- Designing robust evaluation methodology
- Production-quality systems for hardware prototypes
- Agentic workflows for chip design and verification
- Structured process graph for debugging
Hiring Activity
45 roles · 45 in 30d
Department
Seniority
Notable leadership hires: Director of Software Engineering
Company intelligence
