**Job Title** Staff Software Engineer - Data Pipelines for AI + Chip Design **Job Description** **Staff Software Engineer — Data Pipelines for AI + Chip Design** **About the job** **Why this matters** At Cognichip, we’re building at the intersection of hardware, software, and AI. Our platform depends on complex data systems that connect scientific experimentation, chip-design workflows, simulation outputs, model training, and engineering feedback loops. As a Software Engineer focused on Data Pipelines, you’ll help build and evolve the data engine behind our AI-driven semiconductor design platform. This is a high-ownership role for someone who can work across testing, debugging, feature development, infrastructure improvement, and close collaboration with scientists and chip experts. The role is technically demanding, but highly rewarding: you’ll work on systems with many moving parts in a domain where deep engineering skill, speed, and quality all matter. **What you'll do** - Own the data-engine reliability loop.- Run end-to-end tests, triage failures, diagnose root causes, plan fixes, and verify improvements across the core components of Cognichip’s data engine.- Build and evolve data pipelines.- Design, develop, test, and improve sophisticated data-processing systems that support AI workflows, chip-design experimentation, and scientific analysis.- Drive features from idea to release.- Take feature requests from scope and specification through implementation, testing, - documentation, and delivery.- Create useful operational visibility.- Build high-quality dashboards, logs, CLI surfaces, and documentation that help engineers, scientists, and chip experts understand and use the system effectively.- Improve infrastructure over time.- Proactively reduce errors, improve compute and disk efficiency, address technical debt, and keep the system aligned with real user needs. **What You Bring** - Strong software engineering experience building, testing, and maintaining complex systems with many interacting components.- Hands-on experience with data pipelines, backend systems, infrastructure tooling, workflow systems, or internal engineering platforms.- Ability to debug difficult problems across data, code, infrastructure, and user workflows.- Strong coding skills, especially in Python, with good practices around testing, maintainability, and documentation.- Experience turning ambiguous requests into scoped plans, implemented features, and reliable releases.- Clear communication skills and the ability to work effectively with software engineers, scientists, ML researchers, and chip-design experts.- A strong sense of ownership: you identify what needs to improve, execute carefully, and verify that the result works. **Bonus Points** - Experience with dashboards, observability systems, experiment tracking, or internal developer tools.- Background with ML pipelines, scientific computing, simulation workflows, or large-scale experimental data.- Prior exposure to semiconductor design, EDA tools, chip-design workflows, or hardware verification.- Experience working directly with researchers, scientists, hardware engineers, or other deeply technical users.