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.  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, PsiQuantum closed a $1 billion funding 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's most 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 Zennström.  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 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. It represents a fundamental shift, and a path to mastering challenges that cannot be solved any other way. The potential is enormous, and we now have the funding, the government validation, and the sites under construction to make it real.  Job Summary: We're looking for a System Modeling and Simulation Lead to build predictive, system-level models of our quantum computer based on photonic, electronic, and other system descriptions together with measured or specified component performance. The models will translate losses, noise, timing jitter, stability, crosstalk, false-count rates, and related nonidealities into key quantum performance metrics. This role will also create validated subsystem abstractions that conceal unnecessary implementation detail while preserving the behavior and control knobs needed by validation, architecture, and design teams for predictive analysis and model closure. These capabilities must be delivered through user-friendly, well-documented interfaces so teams across the organization can configure, run, and interpret approved models without needing to understand their internal implementation. Responsibilities: • Build predictive system-level quantum performance models • 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. Experience/Qualifications: • Education & experience • MS in Systems Engineering, Applied Mathematics, Physics, Applied Physics, Electrical Engineering, Computer Engineering, or a related field with 10+ years of relevant industry experience, or • PhD in a similar field with 6+ years of relevant industry experience. • Systems-modeling and signal-processing background • Strong foundation in systems modeling for complex, multi-domain physical systems, with the ability to connect component behavior and physical nonidealities to system-level performance metrics. • Strong understanding of signal processing, noise analysis, timing and frequency-domain behavior, stochastic processes, and statistical data analysis. • Modeling & software • Advanced proficiency in Python scientific computing and experience building production-quality simulation or analysis software that is usable by engineers beyond the development team. • Demonst…