Own the end-to-end telemetry design for the quantum machine, spanning IoT telemetry (lab equipment/instrumentation) and application telemetry (measurement, tuning, control, and execution workflows). Design and implement robust data acquisition from lab instruments and facility equipment used during qubit measurement and control, with attention to reliability, security, and operational safety. Define telemetry schemas, event taxonomies, and logging standards that enable reproducible diagnostics across experiments, environments, and hardware revisions. Build and operate scalable pipelines for gathering, buffering, transforming, and shipping telemetry data streams to downstream storage and analytics systems. Enable human-in-the-loop observability: dashboards, alerts, and automated reports. Enable machine-in-the-loop observability: provide well-structured data that other software systems and/or AI agents can consume for automated monitoring and feedback loops. Partner with experimentalists, hardware teams, and control-software developers to translate real debugging needs into well-scoped telemetry features and work items. Use and help standardize AI-assisted tooling for log analysis, anomaly detection, and troubleshooting automation (where appropriate). Contribute to software engineering best practices (code reviews, testing, CI/CD, documentation) in a fast-moving R&D environment. Master's Degree in Computer Science, Software Engineering, Mathematics, Physics, Physical Sciences, or related field AND software industry experience, including developing commercial software, scientific computing applications, or multi-component systems o OR Bachelor's Degree in Computer Science, Software Engineering, Mathematics, Physics, Physical Sciences, or related field AND proven software industry experience, including developing commercial software, scientific computing applications, or Demonstarted programming and software engineering experience, including shipping and operating production-quality services or critical internal systems. Strong programming skills in Python (or equivalent), including writing maintainable, testable code; experience with async I/O and concurrency is a plus. Ability to collaborate effectively across disciplines; very strong written and spoken communication, and a documentation-first mindset. Proactive, self-driven execution and comfort working in an evolving, fast-paced research environment. Hands-on experience designing or implementing telemetry/observability: structured logging, metrics, traces, distributed debugging, and operational runbooks. Experience building IoT or edge-to-cloud systems, ideally on Azure; familiarity with services such as Azure IoT Hub/IoT Central, Azure Data Explorer, Stream Analytics, or equivalent. Data engineering fundamentals for high-volume time-series or event streams: schema/versioning strategy, data quality, retention, and efficient querying. Experience developing software that interacts with external hardware (scientific instruments, robotics, embedded devices, or similar), including debugging communications with hardware. Doctorate in Computer Science, Software Engineering, Mathematics, Physics, Physical Sciences, OR related field o OR Master's Degree in Computer Science, Software Engineering, Mathematics, Physics, Physical Sciences, OR related field AND demonstrated software industry experience, including developing commercial software, scientific computing applications, multi-component systems o OR Bachelor's Degree in Computer Science, Software Engineering, Mathematics, Physics, Physical Sciences, OR related field AND proven software industry experience, including developing commercial software, scientific computing applications, multi-component systems Experience with observability platforms and dashboarding/alerting (e.g., Grafana, Kusto dashboards, or equivalent). Familiarity with scientific Python stack (NumPy, pandas/xarray, SciPy, visualization libraries). Experience with instrumentation control and lab automation frameworks (e.g., QCoDeS) and/or research-lab measurement environments. Experience designing data models for time-series data, experiment metadata, and traceability across device revisions and test conditions. These requirements include, but are not limited to the following specialized security screenings: Ability to leverage AI tools to drive innovation and efficiency (e.g., research gathering, day to day task automation).
