Work with the Measurement team to implement software infrastructure for scalable qubit bring-up routines—turning experimental intent into scalable, efficient, robust, repeatable implementations that run reliably in the quantum machine. Develop Python software that controls and coordinates a complex bring-up, calibration, tuning routines and their steps to execute high-fidelity measurements on qubit chips 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 and contribute to infrastructural software components (e.g. scheduler, resource model, concurrency and parallelism primitives), and reusable building-blocks/steps of the bring-up routines 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 boundaries (e.g. bad weather scenarios, corner cases, scheduling conflicts, dead-locks) and make clear trade-offs between rapid development and long-term robustness. Embody our culture and values. Master's Degree in Computer Science, Software Engineering, Physics, Electrical Engineering, or related field AND software industry experience OR Bachelor's Degree in Computer Science, Software Engineering, Physics, Electrical Engineering, or related field AND solid software industry experience OR equivalent experience. Experience implementing highly concurrent systems, coordinating large numbers of independent and dependent operations while respecting resource e.g., resource-aware concurrency, scalable execution engines, task scheduling systems, workflow orchestration frameworks, low-latency distributed systems; using Python asyncio, Trio, .NET Tasks, Go goroutines, Rust async, or equivalent technologies; experience with debugging execution flows, timing/triggering, dead-locks, starvation, and reliability issues. Proven 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-modelling approaches (e.g., pydantic). Experience designing data pipelines for workflow orchestration, e.g. schemas for data and metadata schemas, provenance, traceability, discoverability. 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. Customer obsession: demonstrated ability to distil requirements from user stories, fit requests into a larger architecture, deliver iteratively, and communicate trade-offs clearly. 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). Demonstrated analytical and problem-solving skills, including comfort working under time pressure and making pragmatic decisions balancing speed, quality, and robustness. Doctorate in Computer Science, Software Engineering, Physics, Electrical Engineering, or related field AND software industry experience OR Master's Degree in a related field AND solid software industry experience OR Bachelor's Degree in a related field AND in-depth software industry experience Prior experience building software for qubit (or closely related) test, characterization, calibration, or bring-up routines; hands-on work with lab instrumentation and measurement workflows is preferred. 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). Familiarity with observability/telemetry and data platforms used for debugging large experimental systems (structured logging, time-series data, Kusto/Azure Data Explorer, or equivalent). Ability to leverage AI tools to drive innovation and efficiency (e.g., research gathering, day to day task automation).
