Staff Software Engineer - Data Pipelines for AI + Chip Design

Cognichip; Redwood City (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Cognichip in Redwood City is seeking a Staff Software Engineer focused on Data Pipelines. In this role, you’ll build and improve the data engine for our AI-driven semiconductor design platform, requiring deep engineering skills and collaboration with experts.

The position emphasizes reliability, testing, and documentation. Ideal candidates will have strong experience in software engineering, particularly with data pipelines and Python. Join us to innovate at the intersection of hardware, software, and AI.

Qualifications

• Strong software engineering experience building complex systems.
• Hands-on experience with data pipelines and infrastructure tooling.
• Ability to debug difficult problems across data, code, and infrastructure.

Responsibilities

• Own the data-engine reliability loop.
• Run end-to-end tests and diagnose failures.
• Build and evolve data pipelines.

Skills

Software engineering experience

Data pipelines

Python coding

Debugging skills

Clear communication

Job description

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.