Define the technical vision and roadmap for the end-to-end system performance model for a topological quantum computing platform, connecting physical qubit behavior to representative quantum workloads and system-level outcomes. Integrate subsystem modeling efforts across quantum error correction, qubit performance, readout, and the pulsing and control chain into a consistent performance-prediction framework. Define interfaces, assumptions, and validation criteria for subsystem models, and work with domain experts to resolve inconsistencies, quantify uncertainty, and close modeling gaps. Develop common frameworks that compose multiple analytical and simulation models. Use integrated models to evaluate architectural trade-offs, identify performance gaps and critical uncertainties, and influence technical roadmaps across multidisciplinary teams. Leverage modern AI tools to accelerate model integration, rapid prototyping, performance analysis, and engineering productivity while maintaining rigorous validation and traceability. Embody our Culture and Values. Doctorate in Physics, Engineering, or related field AND 3+ years experience in industry or in a research and development environment; OR Master's Degree in Physics, Engineering, or related field AND 6+ years experience in industry or in a research and development environment; OR Bachelor's Degree in Physics, Engineering, or related field AND 8+ years experience in industry or in a research and development environment; Ability to use AI tools to improve engineering productivity, including performance analysis, research synthesis, and routine workflow automation. Ability to work effectively in an AI-first environment by incorporating modern AI tools into day-to-day hardware development and decision-making. Doctorate in Physics, Engineering, or related field AND 5+ years experience in industry or in a research and development environment OR Master's Degree in Physics, Engineering, or related field AND 8+ years experience in industry or in a research and development environment OR Bachelor's Degree in Physics, Engineering, or related field AND 12+ years experience in industry or in a research and development environment OR equivalent experience. Demonstrated familiarity with quantum computing systems, including physical qubit modeling or control & readout modeling Experience developing, integrating, or validating quantitative models for complex systems. Experience designing modular, composable modeling frameworks with well-defined interfaces, provenance, and reproducible execution. Demonstrated ability to integrate work across multidisciplinary teams and clearly communicate model assumptions, limitations, uncertainty, and recommendations. Experience applying modern AI tools or agentic workflows to accelerate scientific modeling while maintaining validation and traceability.
QUANTUM ROLE CONTEXT | Appended by Quantum.Jobs v2
Role context:
This principal-level position exists to establish comprehensive computational architecture strategies by bridging physical qubit performance with high-level application benchmarks. Operating at the intersection of domain-specific research teams and system engineering divisions, the role synthesizes isolated hardware, readout, and error-correction models into cohesive predictive simulation environments. By providing unified end-to-end performance predictions, this technical leadership function guides hardware design trade-offs, aligns multidisciplinary engineering efforts, reduces technical risks, and accelerates development roadmaps for full-stack quantum platforms.
Quantum ecosystem relevance:
As quantum computing transitions from isolated physical experiments to integrated enterprise hardware systems, full-system performance modeling becomes critical for hardware design and validation. This role type supports quantum infrastructure development by linking micro-level qubit physics with system-level control architecture and error mitigation frameworks. By establishing robust simulation methodologies and modular evaluation standards, the function strengthens engineering rigor, clarifies architectural bottlenecks, and aids in demonstrating scalable computational viability across the broader quantum technology sector.
Capability signals:
- Extensive expertise in system-level modeling and physical qubit performance analysis
- Track record of designing composable, modular quantitative simulation frameworks
- Proficiency in integrating modern artificial intelligence tools into engineering workflows
- Strong cross-functional leadership in aligning technical roadmaps across physics teams
- Demonstrated proficiency in analyzing error correction and control chain architectures