QuEra Computing Quantum Scientist role focused on system- and algorithm-level performance modeling for neutral-atom quantum computers at QuEra Computing. Develop models to estimate algorithmic fidelity, runtime, and time-to-solution.
Responsibilities
Develop system- and algorithm-level performance models for large-scale neutral-atom quantum computers
Translate device- and architecture-level models into quantitative estimates of algorithmic fidelity, runtime, and time-to-solution
Analyze how hardware constraints and effective error models impact computational performance
Collaborate closely with hardware theory, experimental, and application development teams
Support internal design decisions and external benchmarking efforts
Qualification
Required
5+ years of experience in performance modeling, simulation, or analysis of quantum systems or other complex computational/physical systems.
Strong background in quantum information science, computational physics, or a closely related quantitative discipline.
Proficiency in numerical modeling and data analysis using Python or Julia.
Demonstrated ability to analyze experimental or simulated data and correlate results with abstracted models.
Strong written and verbal communication skills for technical documentation and cross-disciplinary collaboration.
Preferred
Experience with benchmarking or performance modeling in quantum computing platforms (neutral atoms, ions, superconducting qubits, or similar).
Experience developing or using abstracted error and performance models to evaluate system- and algorithm-level behavior.
Prior participation in federally funded research programs (e.g., DARPA, DOE) or large collaborative research efforts.
Track record of publications or technical contributions related to system-level performance analysis or benchmarking.
QuEra is committed to cultivating a diverse work environment and is proud to be an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.