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 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: 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 ExecutiveChairman, 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 NiklasZennstrom.

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, includingAirbus,LockheedMartin, 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.

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 interpretphotonic, electronic, and mixed-domain system descriptions, component models, characterization data, and system configuration inputs.
  • Translate component and interface performance - includingloss, noise, jitter, drift and stability, crosstalk, and false-count rates - into end-to-end predictions of key quantum system metrics.
  • Develop models that supportarchitecture trades, requirement allocation, performance budgeting, and design decisions before full hardware is available.
  
  
  
  
• Develop coherent, reusable system abstractions
  
  
  
  • Createreduced-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 thenecessary 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 maintaincoherence 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 againstcomponent, subsystem, and integrated-system measurements.
  • Quantify prediction error and uncertainty usingsensitivity analysis, Monte Carlo methods, uncertainty propagation, and statistical inference where appropriate.
  • Investigate model-to-hardware discrepancies, identify missing physics or incorrect assumptions, and drivemodel closure with documented evidence.
  
  
  
  
• Build usable simulation capabilities that scale
  
  
  
  • Develop maintainable, modular simulation software inPython, with automated tests, version control, clear APIs, and reproducible execution.
  • Provide auser-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 withrequirements, characterization data, system configurations, and hardware revisions, while improving execution efficiency without compromising accuracy or traceability.
  • Create a workflow that enablesother 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 throughclear 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 with10+ years of relevant industry experience, or
  • PhD in a similar field with6+ years of relevant industry experience.
  
  
  
  
• Systems-modeling and signal-processing background
  
  
  
  • Strong foundation insystems modeling for complex, multi-domain physical systems, with the ability to connect component behavior and physical nonidealities to system-level performance metrics.
  • Strong understanding ofsignal processing, noise analysis, timing and frequency-domain behavior, stochastic processes, and statistical data analysis.
  
  
  
  
• Modeling & software
  
  
  
  • Advanced proficiency inPython scientific computing and experience building production-quality simulation or analysis software that is usable by engineers beyond the development team.
  • Demonstrated experience withgraph-based system representations, numerical simulation, and model validation for complex physical systems.
  • Experience using and championingmodern agentic software-development best practices.
  • Proficiency withmodern version-control tools and collaborative workflows, particularly Git, including branching, code review, and managing contributions from multiple developers.
  
  
  
  
• Collaboration & communication
  
  
  
  • Exceptional written and verbal communication skills; able to explain model assumptions, uncertainty, limitations, and technical conclusions to both domain experts and system-level decision makers.
  
  
  
  
• Mindset
  
  
  
  • Rigorous and intellectually curious; comfortable working across abstraction levels, challenging assumptions, and iterating as designs and measured data evolve.
  
  
  
  

PsiQuantum provides equal employment opportunity for all applicants and employees. PsiQuantum does not unlawfully discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, military or veteran status, marital status, domestic partner status, sexual orientation, genetic information, or any other basis protected by applicable laws.

Note: PsiQuantum will only reach out to you using an official PsiQuantum email address and will never ask you for bank account information as part of the interview process. Please report any suspicious activity to recruiting@psiquantum.com.

We are not accepting unsolicited resumes from employment agencies.