Software Architect – Manufacturing Test

Cerebras Systems•Sunnyvale, CA

11h•$204,000 - $245,000

About The Position

As the Software Architect for Cerebras' manufacturing test platform, you will lead a team of Full Stack Engineers in designing and delivering the end-to-end software systems that power manufacturing test across every stage of our product lifecycle — from individual components to complete Cerebras systems. The platform spans both cloud infrastructure and physical client-server infrastructure deployed across our manufacturing facilities, and you will own the technical vision, architecture, and roadmap across the full stack. Working closely with hardware design, test engineering, operations, data analytics, and reliability teams, you will translate complex, cross-functional requirements into a cohesive software architecture that drives manufacturing efficiency, quality, and scale.

Requirements

Bachelor's or Master's degree in computer science, computer engineering, or a related field.
8+ years of professional software engineering experience, including 2+ years in a technical leadership, staff engineer, or architect role.
Demonstrated experience designing and delivering complex, full-stack software systems at scale, including distributed systems and data-intensive applications.
Strong proficiency in at least one advanced programming language (e.g. Python, C++) and deep familiarity with modern full-stack development practices.
Experience architecting software for hardware manufacturing environments, such as manufacturing test automation, MES/test data systems, or manufacturing quality control.
Experience architecting and building client-server software, including designing the protocols, APIs, and deployment patterns that connect on-site infrastructure to broader platform services.
Experience with cloud platforms (e.g. AWS, GCP), including infrastructure-as-code, CI/CD, and production operations.
Experience designing systems backed by both SQL databases (e.g. PostgreSQL, MySQL) and NoSQL databases (e.g. MongoDB, Redis), including schema design, performance tuning, and data modeling.
Solid grounding in front-end technologies and frameworks (e.g. HTML, JavaScript, modern UI frameworks) and the ability to make sound architectural decisions about UI/API boundaries.
Track record of mentoring engineers, leading technical projects end-to-end, and partnering effectively with cross-functional stakeholders.

Nice To Haves

Experience building distributed data platforms that synchronize across multiple sites or facilities.
Experience with data engineering, data analytics, and/or business intelligence platforms, and partnering with data teams on reporting and visualization.
Strong UI/UX sensibility and experience guiding the design of tools used by technical operators.
Experience with networking and cybersecurity considerations in industrial or manufacturing settings.

Responsibilities

Define and own the technical architecture and long-term roadmap for the manufacturing test software platform, including test execution frameworks, user interfaces, distributed data storage, cloud services, on-site client-server systems, and reporting.
Lead, mentor, and grow a team of Full Stack Engineers, setting technical standards for code quality, design patterns, testing, documentation, and operational excellence.
Partner with hardware engineers, test developers, data engineers, operations, and reliability teams to translate business and engineering requirements into clear, scalable software designs.
Drive key architectural decisions across the stack — from front-end frameworks and API design to database schemas, distributed data synchronization, cloud deployments, and on-prem infrastructure across multiple manufacturing facilities.
Conduct design and code reviews, guide technical trade-offs, and ensure the team is building secure, reliable, and maintainable systems.
Collaborate with engineering leadership on planning, prioritization, and delivery commitments, and represent the platform in cross-functional technical discussions.
Identify opportunities to improve manufacturing efficiency, quality, and scalability through better tooling, automation, data infrastructure, and platform capabilities.