Software Engineer, Quantum Systems & Digital Engineering

Software Engineer, Quantum Systems & Digital Engineering

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.

Job 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.