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# Staff Chip Design Engineer

- **Company:** COGNICHIP, INC.
- **Location:** Redwood City, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Communications Protocols, Ethernet, Human-Computer Interaction, Python (Programming Language), Machine Learning, Open Source Technology, PCI Express, SystemVerilog, Vivado, Serial Peripheral Interface, Fpga Hardware, Deep Learning, Information Technology, Physical Design
- **Published:** November 9, 2026
- **Apply:** https://www.juju.com/job/28_25303_166946170cebdabef0d59d193b8ae66f

## About the Role

* Masters in Computer Science, Electrical Engineering, or a closely related field
 * 10-15 years of experience in RTL design, or a combination of design and verification
 * Proficiency in SystemVerilog and Python languages
 * Excellent written and verbal communication skills, esp to convey chip design knowledge to scientists/engineers in other fields
 * Comfortable working in a dynamic, research-heavy startup environment
 * Demonstrated coursework or project experience in machine learning and/or deep learning

Bonus Points

 * Personal projects showcasing innovation, creativity, and continuous learning
 * Knowledge of industry-standard communication protocols (SPI, I2C, AXI, Ethernet, PCIe, DDR5, etc)
 * Knowledge of open-source tools and contribution practices (Verilator, CocoTB, Yosys, OpenSTA, etc)
 * Experience with multiple areas of chip flow (RTL design, validation, synthesis, physical design, etc)
 * Experience with FPGA development (Vivado, Vitis, Quartus, etc), front end, IP integration, and/or back end

## Description

* Serve as the domain experts, embedding chip design experience into a team creating AI chip design assistants
 * Bridge research and production. Collaborate closely with ML and SW teams to integrate experimental models into scalable pipelines.
 * Define data-processing pipelines, training orchestration, and evaluation frameworks to accelerate iteration on large-scale experiments.
 * Analyze and iterate. Dive deep into large datasets-experimental logs, simulation outputs, and user interaction traces-to diagnose model behavior, surface failure modes, and drive evidence-based improvements.
 * Publish and present. Document findings in technical reports, contribute to open-source projects, and present work internally (and externally, when appropriate) to help shape the broader AI research community.