About the role
Work with the Measurement team to implement and maintain measurement, characterization, and bring-up routines for qubit devices—turning experimental intent into robust, repeatable software workflows used in the lab and in the quantum machine. Develop Python software that controls and coordinates a complex instruments rack (timing, triggering, waveform generation, acquisition, and metadata capture) to execute high-fidelity experiments reliably and safely. Partner with scientists and engineers to translate user stories into
requirements; propose designs that fit the larger bring-up architecture and iterate based on feedback from day-to-day lab usage. Build reusable building blocks (drivers/wrappers, experiment templates, calibration primitives, analysis utilities, configuration/schema models) that enable rapid development of new routines. Ensure measurement data is high quality and traceable: consistent metadata, validation, versioning, and reproducible analysis pipelines. Contribute to software engineering best practices: code reviews, testing, CI/CD, packaging, documentation, and on-call/triage support as needed in a fast-moving environment. Troubleshoot end-to-end issues across software and hardware boundaries (instrument communications, timing, signal integrity symptoms reflected in data) and make clear trade-offs between rapid bring-up and long-term robustness. Doctorate in Computer Science, Software Engineering, Mathematics, Physics, Physical Sciences, or related field AND 1+ year(s) software industry experience, including research and/or development of commercial software, compilers, scientific computing applications, or multi-component systems OR Master's Degree in Computer Science, Software Engineering, Mathematics, Physics, Physical Sciences, or related field AND 3+ years software industry experience, including research and/or development of commercial software, compilers, scientific computing applications, or multi-component systems OR Bachelor's Degree in Computer Science, Software Engineering, Mathematics, Physics, Physical Sciences, or related field AND 4+ years software industry experience, including research and/or development of commercial software, compilers, scientific computing applications, or multi-component systems OR equivalent experience. 4+ years programming experience in related programming languages. 4+ years experience in a collaborative environment. Ability to leverage AI tools to drive innovation and efficiency (e.g., research gathering, day to day task automation). Doctorate in Computer Science, Software Engineering, Physics, Electrical Engineering, or related field AND 2+ years software industry experience OR Master's Degree in a related field AND 4+ years software industry experience OR Bachelor's Degree in a related field AND 6+ years software industry experience 2+ years of prior experience building software for qubit (or closely related) test, characterization, calibration, or bring-up routines, including hands-on work with lab instrumentation and measurement workflows. Experience with instrumentation control and lab automation frameworks (e.g., QCoDeS or similar), including driver development and integration with vendor SDKs. Experience developing software that interacts with sophisticated hardware (instrument racks, RF/microwave equipment, digitizers, AWGs, DC sources, cryogenic/facility sensors, or similar), including debugging communications, timing/triggering, and reliability issues. Strong Python software engineering skills: writing maintainable, testable code; solid grasp of language idioms and the standard library; experience with the scientific Python stack (e.g., NumPy, SciPy, pandas, xarray) and typed/data-modeling approaches (e.g., pydantic). Familiarity with modern development operations and tooling such as CI&CD on platforms like GitHub and Azure DevOps, and Python tooling (pip/uv, ruff, pre-commit, packaging and dependency management). Experience designing experiment abstractions, configuration systems, and data/metadata schemas for traceable measurement at scale. Experience with scientific data analysis pipelines, statistical methods, optimization/fitting, and uncertainty quantification applied to device characterization. Experience improving engineering quality in research environments (test strategies for hardware-interfacing code, simulation/mocking of instruments, reliability engineering). Collaborative engineering experience working with other software developers on shared codebases: design discussions, code reviews, feature ownership, and incorporating feedback from both peers and end users. Strong analytical and problem-solving skills, including comfort working under time pressure and making pragmatic decisions balancing speed, quality, and robustness. Customer obsession: demonstrated ability to distil
