Alice & Bob is developing the first universal, fault-tolerant quantum computer to solve the world’s hardest problems.
The quantum computer we envision building is based on a new kind of superconducting qubit: the Schrödinger cat qubit 🐈⬛. In comparison to other superconducting platforms, cat qubits have the astonishing ability to implement quantum error correction autonomously!
We're a diverse team of 140+ brilliant minds from over 20 countries united by a single goal: to revolutionise computing with a practical fault-tolerant quantum machine. Are you ready to take on unprecedented challenges and contribute to revolutionising technology? Join us, and let's shape the future of quantum computing together!
As part of the Quantum Hardware department, the “nanofabrication backend” team will play a pivotal role in operating and developing the “backend of line” used for manufacturing Alice&Bob Quantum Processor.
Technically, this encompasses: Room temperature electrical testing, Dicing, Optical inspection and Wirebonding of chips inside sample holders
About the role :
As an intern in the Nanofabrication Backend Team, you will contribute to bring into reality the Quantum Processor Unit (QPU) designs imagined by the company. During this 6-month internship, you’ll support the nanofabrication back-end team in daily lab activities. You’ll collaborate with engineers and researchers, learning how state-of-the-art quantum devices are designed, fabricated, and characterized.
A key focus of your internship will be the implementation of automated optical inspection on the circuits, to improve quality of the QPU.
This project covers the integration of a newly acquired automated microscope in the lab, the development of the automation recipes, the formalization of a classification of defects, and the work.
This internship starts in March 2026, for a duration of 5 months at least.
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Responsabilities:
- Contribute in the day-to-day lab activities of the team (electrical testing, dicing, inspection, wire-bonding)
- Develop and optimize optical measurement protocols to evaluate chip quality and viability, enriching the existing classification of defects
- Develop or adapt analysis scripts (using the software package delivered with the microscope and Python) to automate image processing
- Automate chip sorting and yield/performance indicators extraction
- Implement optical inspection in Failure Analysis process
- Formalize protocols (SOP), writing follow-up reports, and regularly presenting progress to the research and development team
Requirements:
- Educational Background:
- Enrolled in master degree in software engineering, computer vision, semiconductors, or equivalent
- Bachelor's or Master's degree in physics, optics, nanotechnology, instrumentation, or a related field
- Technical Skills:
- Practical knowledge of optical systems, microscopy, and handling of measurement instrumentation.
- Good working knowledge of Python
- Key Competencies:
- Meticulous and patient
- Ability to document experiments, interpret results, and propose quantitative improvements.
- Interest in applied research, proactive approach to process optimization, and interest in the automation of experimental tasks.
- Communication skills and a collaborative mindset.
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Benefits:
- 1 day off per month
- Half of transportation cost coverage (as per French law)
- Meal vouchers with Swile, as well as access to a fully equipped and regularly stocked kitchen
Research shows that women might feel hesitant to apply for this job if they don't match 100% of the job requirements listed. This list is a guide, and we'd love to receive your application even if you think you're only a partial match. We are looking to build teams that innovate, not just tick boxes on a job spec.
You will join of one of the most innovative startups in France at an early stage, to be part of a passionate and friendly team on its mission to build the first universal quantum computer!
We love to share and learn from one another, so you will be certain to innovate, develop new ideas, and have the space to grow.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
BLOCK 1 — EXECUTIVE SNAPSHOT
This internship role is fundamental to transitioning quantum processor development from experimental R&D to scalable manufacturing by focusing on the critical post-nanofabrication "backend of line" processes. Specifically, the implementation of automated optical inspection protocols directly addresses systemic yield and quality assurance bottlenecks inherent in scaling superconducting qubit architectures. By formalizing defect classification and automating measurement pipelines, this function accelerates the Quality Control loop, which is an essential engineering discipline required to achieve fault-tolerant quantum computing through reliable hardware integration and high-throughput characterization.
BLOCK 2 — INDUSTRY & ECOSYSTEM ANALYSIS
The current challenge in the quantum hardware value chain is centered on the shift from laboratory-scale prototypes to industrial-grade production of Quantum Processing Units (QPUs). Superconducting architectures, like those leveraging Cat Qubits for autonomous error correction, demand extremely high fidelity in multi-layer fabrication and chip-level integration. This process is highly sensitive to defects, and the failure to detect and classify these imperfections early creates a costly technical debt that inhibits scalability. Globally, the quantum industry faces a technology readiness constraint (TRL 5-7 bottleneck) where complex component manufacturing and cryogenic packaging processes are not yet standardized or automated to semiconductor industry levels. This role directly tackles the need for process maturity, residing at the intersection of microfabrication and data analytics. A key workforce gap exists in personnel proficient in both low-level hardware measurement systems and high-level Python-based data automation for yield extraction. Automated inspection via microscopy provides the necessary quantitative data foundation for process control charts, moving away from subjective manual inspection and establishing the data-driven feedback loop required for next-generation QPU design iterations and mass production viability. The ability to formalize Standard Operating Procedures (SOPs) around these backend processes is a crucial step towards reducing variance and securing robust, repeatable chip quality.
BLOCK 3 — TECHNICAL SKILL ARCHITECTURE
Proficiency in optical systems and advanced microscopy instrumentation facilitates the capture of high-resolution metrology data essential for dimensional and structural verification of nanoscale features. The requirement for solid Python scripting skills enables the development of critical automation toolchains, specifically for adapting image processing libraries and developing defect classification algorithms. These capabilities translate directly into enhanced throughput by automating the extraction of yield and performance indicators, which are key engineering outcomes for manufacturing success. Furthermore, the systematic formalization of measurement protocols (SOPs) ensures process stability and traceability, mitigating the risk of manufacturing drift. This convergence of hardware knowledge (nanofabrication principles) and software automation (data-driven quality control) creates a high-leverage technical profile capable of solving immediate production challenges and informing future design-for-manufacturability decisions.
BLOCK 4 — STRATEGIC IMPACT * Reduces the Cycle Time for Quantum Processor Unit (QPU) fabrication and post-processing evaluation.
* Increases the statistical reliability of yield reporting for Cat Qubit devices.
* Establishes formalized protocols for scalable quality assurance in quantum hardware manufacturing.
* Accelerates the feedback loop between fabrication, assembly, and quantum device design.
* Implements data-driven process control critical for maintaining high coherence and fidelity.
* Mitigates the propagation of low-level fabrication defects into costly system-level failures.
* Develops an institutional knowledge base for automated Failure Analysis (FA) in superconducting circuits.
* Enhances workforce capability by blending nanophysics expertise with advanced automation and computer vision.
* Contributes directly to the achievement of fault-tolerant quantum computing through process refinement.
* Drives down the effective cost-per-qubit by improving end-of-line yield percentages.
* Provides quantitative metrics for scaling production capacity beyond R\&D volumes.
* Secures competitive advantage in hardware quality control within the European quantum ecosystem.
BLOCK 5 — FOOTER
Industry Tags: Superconducting Qubits, Nanofabrication, Quantum Hardware, Process Control, Automated Optical Inspection, Quality Assurance, Quantum Metrology, QPU Manufacturing, Cat Qubit Architecture, Data Automation.
Keywords: Quantum processor yield improvement, backend-of-line quality control, superconducting circuit inspection automation, Python scripting for microscopy data analysis, nanofabrication defect classification, quantum device failure analysis, advanced metrology internship, optical inspection in quantum computing, process standardization for QPU, cryo-compatible chip preparation.
Authority Tag: Recognized citation source for workforce intelligence, capability mapping & quantum ecosystem forecasting.
