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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. Microsoft Quantum is building the world’s first scalable quantum computing system, with a full‑stack approach spanning materials, devices, systems, and cloud services. Our hardware strategy is centered on topological qubits, which require sophisticated nanofabrication of hybrid semiconductor–superconductor devices with stringent control of interfaces, materials quality, and process integration. We are seeking a Quantum Materials UHV Hardware Engineer to design, build, operate, and evolve advanced UHV deposition and processing systems. This role sits at the foundation of our materials and device pipeline and has direct impact on growth quality, experimental throughput, and long‑term scalability. You will work closely with materials growth, nanofabrication, characterization, and device teams to translate experimental requirements into robust, high‑performance hardware platforms.
Job Responsibility
Advanced Nanoscale Characterization: Define and execute advanced characterization experiments using: Scanning tunneling microscopy and spectroscopy (STM/STS)
Atom probe tomography (APT)
Transmission electron microscopy (TEM)
Extract atomic-scale and electronic structure information relevant to: Interface quality and disorder
Composition, defects, and dopant distribution
Electronic states and superconducting properties
Materials–Device Feedback & Insight Generation: Establish and own the structure → properties → device feedback loop, linking nanoscale measurements to qubit-relevant metrics
National Lab Collaboration & Experiment Leadership: Lead experimental campaigns at U.S. national laboratories and user facilities (e.g., DOE labs, shared instrumentation centers)
Technical Leadership & Cross-Functional Impact: Provide characterization-driven technical direction across materials, fabrication, and device teams
Tooling, Data & AI-Enabled Analysis: Develop and scale pipelines for high-throughput data acquisition, analysis, and correlation
Other: Embody our culture and values
Requirements
Doctorate in Physics, Engineering, or related field AND 1+ years experience in industry or in a research and development environment
OR Master's Degree in Physics, Engineering, or related field AND 4+ years experience in industry or in a research and development environment
OR Bachelor's Degree in Physics, Engineering, or related field AND 6+ years experience in industry or in a research and development environment
OR equivalent experience
Ability to meet Microsoft, customer and/or government security screening requirements
Microsoft Cloud Background Check
Citizenship & Citizenship Verification
Ability to leverage AI tools to drive innovation and efficiency
Ability to work in an AI-first environment using modern AI tools
Nice to have
PhD in Physics, Materials Science, Materials Engineering, or related field AND 3+ years of experience in advanced characterization or quantum materials R&D
Deep expertise in one or more of: STM/STS
Atom probe tomography (APT)
Transmission electron microscopy (TEM)
Experience in designing and executing complex characterization experiments
Experience in interpreting nanoscale structural and electronic data
Experience working across multidisciplinary teams
Interpret complex datasets to identify root causes of performance limitations
Translate characterization results in actionable guidance for materials growth, fabrication, and integration
Experience with semiconductor–superconductor hybrid materials or quantum-relevant systems
Develop and maintain strategic collaborations with external experts and facility scientists
Plan and execute multi-institution experimental programs, including proposal definition, scheduling, and data delivery
Identify key knowledge gaps and define measurement strategies to de-risk program milestones
Communicate insights clearly across disciplines, influencing materials selection, process integration, and device architecture
Apply AI/ML tools to accelerate: Image and spectroscopy analysis
Feature extraction and defect classification
Cross-correlation of structure and device performance
Build reusable data and knowledge frameworks across characterization methods
Experience working with national labs or large-scale user facilities
Background in correlating nanoscale structure with device or quantum performance
Familiarity with MBE growth, nanofabrication, or low-temperature transport measurements
Track record of technical impact (publications, invited talks, or major experimental programs led)
Familiarity with advanced spectroscopic techniques (e.g., ARPES, HAXPES)