Software Engineer - Data Pipelines AI + Chip Design

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.

• 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.

• 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.

• 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.