Senior Quantum Software Engineer - Telemetry (Internet-of-Things & Application)

Microsoft Quantum has assembled a talented and diverse international team to create the world’s first scalable quantum computing system. The research effort includes a diverse staff of theoretical and experimental physicists, hardware designers and software engineers around the world collaborating in a very fast-paced environment, where good communication and good documentation are key to the success of the program.

This role sits on the software team building the control and test stack for Microsoft’s topological qubit chips and the broader quantum machine we are engineering. As a Senior Quantum Software Engineer, you will own the design and implementation of telemetry that makes the system observable, diagnosable, and operable—from lab instruments and facility equipment to application-level measurement, tuning, and control workflows.

At Microsoft Quantum, we aim to empower science and scientists to solve the world’s biggest problems by realizing advanced computing platforms at the intersection of high-performance computing, artificial intelligence, and quantum information technology. Microsoft Quantum will change the world of computing and help solve some of humankind’s currently unsolvable problems. For more information about our team, visit https://www.microsoft.com/en-us/quantum.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Role Summary

We’re looking for a senior software engineer to lead telemetry for the quantum machine: instrument and lab-equipment telemetry (Internet-of-Things, IoT) plus application telemetry emitted by qubit measurement, calibration, tuning, and control routines. You will define the telemetry architecture, implement reliable data collection and shipping, and ensure the resulting logs and data streams can be used for rapid diagnostics, troubleshooting, dashboarding, and alerting. You’ll also help apply AI-assisted workflows to accelerate debugging and automate operational insights as the system scales.

You will work closely with experimental physicists, hardware engineers, and control-software developers. The role is expected to involve hands-on collaboration with lab environments and instrumentation, alongside cloud and data-platform development. You will participate in design reviews with team members and stakeholders and select from available technologies, considering how to improve features. You’ll build software infrastructure and tools to support scalable quantum computing and track, resolve, and debug issues. You will ensure delivery of high-quality features, contribute to end-to-end system and integration testing, and use telemetry data to assess quality and monitor how features are operating. You’ll develop components of infrastructure and implement build pipelines and continuous integration.

Why This Role

This is a unique opportunity to contribute to Microsoft’s Quantum Program, dedicated to building a scalable quantum computer to tackle humanity’s most complex challenges. You’ll help make the quantum machine observable and operable as it scales—turning raw signals, logs, and equipment health data into the insights and automation that keep cutting-edge hardware and software moving forward.

· 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