Staff Software Engineer - Data Pipelines for AI + Chip Design
Job Description
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.