Data Platform Software Engineer

Responsibilities

• Key member of the software solutions team responsible for developing and deploying software for test systems, product platforms and manufacturing data management.
• Design, build, and maintain scalable data pipelines that ingest data from EDA tools, simulation frameworks, verification systems, lab equipment, and manufacturing test platforms.
• Develop robust ETL/ELT workflows to transform netlists, layout metadata, waveform data, logs, and yield data into structured, analysis-ready datasets.
• Integrate disparate engineering systems across design, verification, tapeout, and silicon validation environmen

Additional Information

PsiQuantum's mission is to build the first useful quantum computers-machines capable of delivering the breakthroughs the field has long promised. Since our founding in 2016, our singular focus has been to build and deploy million-qubit, fault-tolerant quantum systems. Quantum computers harness the laws of quantum mechanics to solve problems that even the most advanced supercomputers or AI systems will never reach. Their impact will span energy, pharmaceuticals, finance, agriculture, transportation, materials, and other foundational industries. Our architecture and approach is based on silicon photonics. By leveraging the advanced semiconductor manufacturing industry-including partners like GlobalFoundries-we use the same high-volume processes that already produce billions of chips for telecom and consumer electronics. Photonics offers natural advantages for scale: photons don't feel heat, are immune to electromagnetic interference, and integrate with existing cryogenic cooling and standard fiber-optic infrastructure. In 2024, PsiQuantum announced government-funded projects to support the build-out of our first utility-scale quantum computers in Brisbane, Australia, and Chicago, Illinois. These initiatives reflect a growing recognition that quantum computing will be strategically and economically defining-and that now is the time to scale. PsiQuantum also develops the algorithms and software needed to make these systems commercially valuable. Our application, software, and industry teams work directly with leading Fortune 500 companies-including Lockheed Martin, Mercedes-Benz, Boehringer Ingelheim, and Mitsubishi Chemical-to prepare quantum solutions for real-world impact. Quantum computing is not an extension of classical computing. It represents a fundamental shift-and a path to mastering challenges that cannot be solved any other way. The potential is enormous, and we have a clear path to make it real. Come join us. Job Summary: Quantum computing promises to solve many important problems that could never be solved on any conventional computer. At PsiQuantum we're building a general-purpose silicon photonic quantum computer to tackle these very problems. PsiQuantum is looking for an experienced data platform engineer who can design, implement and maintain scalable data pipelines that extract, move and transform data across the Photonic Circuits organization. This role optimizes ETL workflows and integrates various data sources (APIs, DBs, streaming systems) and ensures data quality and observability. This role will work closely with the platforms team and the circuit designers, system engineers and validation engineers. The output will enable ease of producing analytics, scaling tape-out and providing well-structured data sets for reporting. Quantum computing requires breakthrough engineering innovations; we expect software solutions that are novel and custom while at the same time, leverage automation and other best practices from the semiconductor industry. Being a startup in an all-new engineering field, the role is expected to be highly collaborative and requires excellent technical skills to execute projects to meet milestones and technical scope. An ideal Data Platform SW Engineer candidate combines strong SW engineering skills with deep appreciation for semiconductor and chip design EDA toolchains. They are experienced in building scalable data pipelines that ingest and transform data from simulation outputs, layout tools, verification systems, test benches, lab measurements, and manufacturing yield reports. Proficient in Python and SQL, they are comfortable working with large structured and unstructured datasets (netlists, GDS metadata, waveform data, logs) and integrating disparate systems across design, verification, and fabrication environments. They understand the importance of data integrity, traceability, and reproducibility in silicon development, and design pipelines with robust validation, versioning, and auditability. The ideal candidate collaborates effectively with circuit designers, CAD/EDA engineers, verification teams, and manufacturing operations, translating complex engineering workflows into reliable, automated data infrastructure that accelerates design iteration, improves yield analysis, and enables data-driven decision-making across the chip lifecycle.

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