Software Engineer, Quantum Systems & Digital Engineering

Infleqtion•Chicago, IL

About The Position

We are seeking a Software Engineer, Quantum Systems & Digital Engineering to develop advanced software tools for the design, simulation, integration, and manufacturing of complex quantum systems. This role will focus on building modular software environments that connect models, data, and engineering workflows across multiple technical domains. Initial applications will include digital-twin and design-automation capabilities for quantum sensing systems, with broader opportunities to contribute to software infrastructure supporting quantum system development, manufacturing, test, and deployment. The ideal candidate combines strong scientific software engineering skills with familiarity in model-based systems engineering, multiphysics simulation, high-performance computing, machine learning and optimization. This individual will work closely with physicists, systems engineers, optical and mechanical engineers, manufacturing teams, and software developers to turn fragmented engineering workflows into scalable, integrated software platforms.

Requirements

BS or MS in Computer Science, Physics, Applied Mathematics, Electrical Engineering, Mechanical Engineering, Systems Engineering, or a closely related field.
Strong scientific or engineering software-development experience, with high proficiency in Python and familiarity with modern numerical and scientific-computing libraries.
Experience developing software that integrates multiple simulation, modeling, engineering, or scientific computing tools.
Strong understanding of numerical methods, optimization, simulation, data analysis, or computational modeling.
Familiarity with systems engineering concepts, including requirements, interfaces, system architecture, verification and validation, trade studies, or multidisciplinary engineering workflows.
Experience with professional software-engineering practices including version control, automated testing, CI/CD, documentation, modular architecture, and maintainable API design.
Demonstrated ability to collaborate effectively with scientists and engineers across multiple technical disciplines.
Demonstrated ability and enthusiasm for using modern AI-assisted development tools to improve software-engineering productivity and quality.

Nice To Haves

Advanced degree in physics, engineering, computer science, applied mathematics, systems engineering, or a related computational discipline.
Experience with model-based systems engineering (MBSE) tools, methodologies, or standards, such as SysML, digital thread approaches, requirements traceability, or model-centric engineering workflows.
Experience with multiphysics or engineering platforms such as Ansys, Zemax OpticStudio, SolidWorks, COMSOL, MATLAB/Simulink, or similar tools.
Experience developing digital twins, simulation orchestration frameworks, engineering automation tools, or model-based design environments.
Experience with machine learning, surrogate modeling, Bayesian optimization, uncertainty quantification, or design-space exploration.
Familiarity with quantum technologies, optical systems, RF systems, atomic physics, precision sensing, or other complex physical systems.
Experience with HPC, GPU acceleration, distributed computing, or cloud-based scientific-computing environments.
Experience applying generative AI, coding agents, or other AI-enabled tools within scientific or engineering software-development workflows.

Responsibilities

Develop and maintain modular software platforms that integrate models, data, and engineering workflows across RF, optical, mechanical, atomic physics, and other technical domains.
Build interfaces between engineering and scientific computing tools, enabling coordinated simulation, analysis, optimization, and design workflows.
Support model-based systems engineering (MBSE) activities by developing software tools that connect requirements, models, interfaces, parameters, verification data, and system-level performance.
Develop HPC- and GPU-enabled workflows for parameter sweeps, sensitivity analysis, tolerance studies, optimization, and computationally intensive simulation.
Apply numerical optimization, reduced-order modeling, surrogate modeling, machine learning, and other data-driven techniques to accelerate engineering design and improve manufacturability.
Develop APIs, data models, schemas, visualization tools, and workflow orchestration capabilities that enable reliable exchange of information across heterogeneous engineering tools.
Collaborate with domain experts to validate models against experimental data and translate engineering needs into maintainable, production-quality software.
Embrace and help establish effective use of AI-assisted software engineering tools to accelerate development, testing, documentation, debugging, code review, and engineering analysis while maintaining strong software quality and technical rigor.