System Modeling and Simulation Lead (Quantum Systems)

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 architectureand approachisbased on silicon photonics. Byleveragingthe advanced semiconductor manufacturing industry, including partners like Global Foundries, we use the same high-volume processes that already produce billions of chips for telecom and consumer electronics. Photonics offers natural advantages for scale: photonsdon'tfeel heat, are immune to electromagnetic interference, and integrate with existing cryogenic cooling and standard fiber-optic infrastructure. That approach has since been tested at the highest level of scrutiny the U.S. government applies to emerging technology, and it has held up. In 2025, PsiQuantumclosed a$1 billionfunding round that valued the company at$7 billion. In 2026, the U.S. Department of Commerce signed a $100 million Letter of Intent under the CHIPS and Science Act, followed by a $125 million agreement with DARPA under Stage C of its Quantum Benchmarking Initiative, PsiQuantum'smost valuable U.S. government agreement to date. This year, we also broke ground on our utility-scale quantum computer in Brisbane, Australia, and continued building out our Chicago site. Victor Peng, our CEO, and co-founder Jeremy O'Brien, now Executive Chairman, lead a team and board that reflects how seriously the world is taking this build, joined this year by Intel's Lip-Bu Tan and Atomico's Niklas Zennstrom. PsiQuantumalso 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 Airbus, 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. Itrepresentsa fundamental shift, and a path to mastering challenges that cannot besolvedany other way. The potential is enormous, and we now havethe funding, the governmentvalidation, andthe sitesunder construction to make it real.

Ingest and interpret photonic, electronic, and mixed-domain system descriptions, component models, characterization data, and system configuration inputs. Translate component and interface performance - including loss, noise, jitter, drift and stability, crosstalk, and false-count rates - into end-to-end predictions of key quantum system metrics. Develop models that support architecture trades, requirement allocation, performance budgeting, and design decisions before full hardware is available. Develop coherent, reusable system abstractions Create reduced-order or behavioral models that connect logical intent to physical-system behavior and support control, calibration, and validation workflows, while masking lower-level implementation details that are not needed by model users. Define stable model interfaces and expose the necessary inputs, outputs, states, uncertainties, and control knobs for architecture, validation, and design teams, as well as control and calibration workflows. Establish clear validity ranges, assumptions, parameter provenance, and fidelity levels, and maintain coherence among logical, physical, and control models as each evolves. Drive model validation and closure Partner with validation, design, and subsystem teams to compare model predictions against component, subsystem, and integrated-system measurements. Quantify prediction error and uncertainty using sensitivity analysis, Monte Carlo methods, uncertainty propagation, and statistical inference where appropriate. Investigate model-to-hardware discrepancies, identify missing physics or incorrect assumptions, and drive model closure with documented evidence. Build usable simulation capabilities that scale Develop maintainable, modular simulation software in Python, with automated tests, version control, clear APIs, and reproducible execution. Provide a user-friendly interface - such as documented Python APIs, configuration-driven workflows, command-line or graphical tools, templates, and clear visualization/reporting - so users across the organization can configure and run approved analyses with minimal support. Create versioned component and subsystem model libraries that remain synchronized with requirements, characterization data, system configurations, and hardware revisions, while improving execution efficiency without compromising accuracy or traceability. Create a workflow that enables other engineers and domain experts to contribute component, subsystem, and detailed-physics models while maintaining common interfaces, review rigor, traceability, and quality standards. Partner cross-functionally and communicate clearly Work closely with architecture, validation, design, photonics, electronics, controls, calibration, detailed physics modeling, and systems engineering teams to define model needs, boundaries, and interfaces. Turn ambiguous performance questions into well-scoped modeling studies with clear assumptions, confidence bounds, and decision-relevant outputs. Communicate model behavior, limitations, dominant sensitivities, and recommended actions through clear documentation and concise technical and executive updates.