Staff Software Engineer - Data Pipelines for AI + Chip Design

**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.