QuEra is seeking Quantum Calibration Engineers to develop, test, and deploy calibration and performance characterization software on QuEra’s neutral-atom quantum computers, translating experimental physics and data analysis into automation to achieve programmability, reliability, and peak performance for research, application development, and commercial uses.
Requirements
- Strong proficiency in Python, including experience with data analysis and visualization libraries (e.g., NumPy, SciPy, Pandas, Matplotlib).
- Hands-on experience working with real experimental or measurement data, including noise handling, curve fitting, and model validation.
- Familiarity with optimization and regression techniques, both classical (e.g., least-squares, nonlinear fits) and statistical (e.g., Bayesian inference, parameter estimation).
- Comfort working in a hardware-facing environment, integrating software with lab instruments, data acquisition systems, or experimental control frameworks.
- Solid understanding of version control (Git) and structured software development practices (testing, modularity, documentation).
- Experience with machine learning or data-driven modeling (e.g., for pattern recognition, drift prediction, or parameter inference).
- Familiarity with Kubernetes, Docker, or distributed analysis systems for scaling calibration and data pipelines.
Responsibilities
- Design data-efficient experimental routines, robust data analyses and troubleshooting workflows to extract key system parameters and insights.
- Develop, refine, and maintain calibration pipelines consisting of complex data flows.
- Develop robust data-fitting and optimization routines.
- Validate, document and track calibration results, ensuring reproducibility and traceability across different hardware and software generations, and across machine deployments.
Other
- Strong problem-solving ability, scientific curiosity, and a willingness to learn new physical systems quickly.
- Eager to grow from a capable engineer into a domain expert in quantum device
- Enjoys hands-on experimentation, iteration, and uncovering structure in data.
- Comfortable working across abstraction layers, from analyzing raw voltage traces to designing experiment-level calibration strategies.
- Collaborative and adaptable; able to operate in an interdisciplinary environment spanning science, software, and hardware teams.
