Experience in mesoscopic or topological physics is valuable but not required. Doctorate in Physics, Engineering, or related field AND 1+ year(s) experience in industry or in a research and development environment, could include completion of a post doctoral research position OR Master's Degree in Physics, Engineering, or related field AND 4+ years experience in industry or in a research and development environment OR Bachelor's Degree in Physics, Engineering, or related field AND 6+ years experience in industry or in a research and development environment OR equivalent experience. Ability to leverage AI tools to drive innovation and efficiency (e.g., performance modeling and analysis, research gathering, day to day task automation). Ability to design and build AI agents/copilots that assist with experiment setup, log triage, measurement report generation, protocol templating, and knowledge retrieval (e.g. instrument manuals, design docs) 1+ year of experience with mesoscopic condensed-matter or topological physics, including exposure to quantum devices, transport, or cryogenic experiments. 1+ year of experience contributing to open-source projects, deployed technical solutions, or published research. Advanced degree (PhD or Master's) in Computer Science, Electrical Engineering, Physics, or a related field. Hands-on experience with modern AI methods, including deep learning, foundation or generative models, probabilistic methods, and AI-enabled scientific workflows. Strong programming skills in Python, C++, or similar languages. Demonstrated initiative, intellectual curiosity, and ability to collaborate across physics, experimental, software, and AI disciplines Excellent problem-solving, communication, and documentation skills. Develop and improve automation pipelines for quantum-device data analysis and experiments. Develop and apply modern artificial intelligence (AI) and machine-learning methods to quantum-device characterization, tuning, and analysis of complex experimental data. Partner with researchers and engineers to integrate machine learning models into simulation and control environments. Contribute to scalable and reproducible experimental analysis workflows, taking increasing ownership as projects mature. Document and present technical solutions to internal and external stakeholders. Embody our Culture and Values.
QUANTUM ROLE CONTEXT | Appended by Quantum.Jobs v2
Role context:
This role exists to bridge physical quantum device engineering and computational automation using artificial intelligence. Positioned between experimental physics and software engineering teams, the position integrates computational intelligence into physical testing workflows. By developing data pipelines and machine learning protocols, personnel in this function accelerate experimental characterization, optimize device tuning, and automate diagnostic routines across complex hardware systems. This technical function helps maintain consistency, speed, and reproducibility throughout ongoing laboratory testing and system optimization processes.
Quantum ecosystem relevance:
The position contributes directly to the hardware enablement layer by applying artificial intelligence to physical quantum device testing and data processing. Complex quantum devices require rapid, high-dimensional characterization and fine control calibration that manual methods cannot efficiently scale. Integrating machine learning architectures into measurement environments improves data extraction accuracy and reduces testing bottlenecks, directly supporting the broader development of functional, scalable quantum hardware systems.
Capability signals:
- Background in applying machine learning models to complex physical system characterization tasks
- Technical expertise in developing scalable data analysis automation pipelines using Python
- Familiarity with mesoscopic physics, cryogenic experimentation, or quantum transport measurements
- Experience building functional computational software, technical agents, or open-source solutions
- Ability to collaborate effectively across experimental physics, AI, and engineering domains
