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!
The Calibration team automatizes calibrations of our cat‑qubit Quantum Processing Unit (QPU) to maximize performance and maintain the processor in working condition. Automatic calibrations generate large volumes of data: calibration results, error logs, performance metrics, and hardware diagnostics. Today, answering simple questions such as “What is the success rate of nightly recalibrations over the last month, and where did they fail?” requires tedious manual log gathering. As Senior Calibration Data Infrastructure Engineer, you will design and implement the data infrastructure that makes these questions trivial to answer. You will build systems to store, organize, and query calibration results, enabling meta‑analysis of time series across performance, hardware failures, and data analysis issues. Your work will empower the team to quantify execution at multiple levels and accelerate the reliability of our QPU operations.
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Responsibilities:
- Design and implement a robust data storage and retrieval system for calibration results, error logs, and performance metrics.
- Develop pipelines to automatically collect, normalize, and index calibration outputs for easy querying and meta‑analysis.
- Build tools and APIs that allow scientists and engineers to quickly answer operational questions (success rates, failure points, drift statistics).
- Implement time‑series analysis frameworks to track calibration dynamics, detect anomalies, and generate reports.
- Establish standards for data schemas, provenance, retention, and reproducibility of calibration results.
- Provide visibility through automated reporting on calibration performance, hardware reliability, and analysis quality.
- Mentor engineers and contribute to long‑term strategy for calibration data infrastructure.
Requirements:
- 5+ years experience in backend engineering, data infrastructure, or DevOps with production systems.
- Strong proficiency in Python and experience with data engineering frameworks (Pandas, SQLAlchemy, Spark, or equivalent).
- Expertise in time‑series databases (TimescaleDB, InfluxDB, Prometheus) and log aggregation systems (ELK stack, Grafana, or similar).
- Proven track record in designing scalable data pipelines and APIs for scientific or hardware‑related data.
- Experience with observability stacks (metrics, logs, traces) and building dashboards for technical users.
- Familiarity with statistical analysis and anomaly detection; ability to collaborate with scientists on model integration.
- Strong understanding of CI/CD, testing, and reproducibility in scientific or hardware‑in‑the‑loop environments.
- Excellent communication skills and ability to translate operational needs into technical solutions.
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Benefits:
- Our success is your success : own it with our BSPCE plan
- Direct IP Compensation: Earn substantial bonuses for driving the core patents that define our quantum architecture.
- Flexible remote policy, up to 40 % a month
- A Parental plan including additional benefits such as crèche support or additional days-off to take care of under 12 years old children
- Subsidized membership withUrban Sports Club
- Mental health support with moka.care
- 25-day vacation policy (as per French law) + RTT
- Half of transportation cost coverage (as per French law), or yearly allowance for the die-hard bicycle users
- Competitive health coverage, with Alan.
- Meal vouchers with Swile, as well as access to a fully equipped and regularly stocked kitchen
- French language courses covered by the company for those interested
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
This role is central to advancing quantum machine operational maturity, transitioning the superconducting cat-qubit architecture from a research-scale system to a reliable, production-grade Quantum Processing Unit (QPU). The function establishes the critical data plane necessary for systematic drift correction, automated performance quantification, and root-cause analysis of physical hardware failures. By engineering robust infrastructure for high-volume, high-velocity calibration telemetry, the position directly mitigates the fundamental challenge of hardware instability, accelerating the path to fault-tolerant, universal quantum computation (FTQC).
The scalability bottleneck in superconducting quantum computing is increasingly shifting from fundamental qubit physics to the engineering of high-uptime, predictable operational workflows. Calibration drift—the time-dependent variation in quantum gate performance—is a persistent challenge requiring continuous, automated feedback loops. In the quantum value chain, this role resides at the crucial intersection of the control electronics layer, the quantum chip, and the diagnostic software stack. The immense volume of time-series data generated by frequent QPU calibrations (e.g., Rabi oscillations, T1/T2 measurements) demands specialized data infrastructure beyond conventional relational databases. Vendor consolidation trends favor platforms that can demonstrably achieve and maintain high-fidelity operations over extended periods, making data-driven calibration a key competitive differentiator. Furthermore, the specialized nature of quantum control demands an integration of data engineering expertise with deep scientific domain knowledge, highlighting a significant workforce gap in the quantum-classical interface domain. The successful deployment of this infrastructure is a necessary condition for achieving the Technology Readiness Level (TRL) required for commercial FTQC systems, directly impacting the economic viability and deployment timeline of cat-qubit technology.
The required technical skill architecture centers on the engineering of a high-throughput, low-latency observability stack optimized for scientific data. Proficiency in Python is the foundational capability for developing modular data ingestion and transformation pipelines (ETL/ELT). Deep expertise in time-series database technologies (e.g., TimescaleDB, InfluxDB, Prometheus) is non-negotiable, as quantum calibration data is fundamentally temporal and requires specialized indexing for drift analysis and rapid query execution. These capabilities are crucial for supporting complex meta-analyses across performance metrics, enabling the systematic identification and modeling of temporal correlations between environmental factors, hardware diagnostics, and algorithmic failure modes. This implementation moves calibration operations from manual scientific logging to an industrial-scale, automated monitoring and feedback loop, enabling predictive maintenance and higher QPU utilization rates. * Enables automated, quantitative performance tracking across the entire Quantum Processing Unit lifetime.
* Reduces latency in identifying and correcting hardware-induced quantum error sources (drift control).
* Establishes auditable data provenance and reproducibility for all quantum experimentation and calibration runs.
* Accelerates the iteration cycle for the Calibration and Quantum Engineering teams by providing instant data access.
* Transforms manually-intensive operational troubleshooting into API-driven, scalable query execution.
* Provides the foundational data layer for implementing future ML-driven predictive calibration models.
* Increases QPU uptime and maximizes available quantum compute resources for commercial partners.
* Standardizes data models for complex control system telemetry, improving cross-functional data consumption.
* Supports rapid triage of system anomalies, decreasing Mean Time to Resolution (MTTR) for critical failures.
* Lays the data groundwork necessary for scaling the QPU array to larger, fault-tolerant qubit counts.
* Contributes to the core Intellectual Property surrounding reliable quantum computer operation.
* Quantifies the effectiveness of new error correction and mitigation protocols in real-time.
BLOCK 5 — FOOTER
Industry Tags: Quantum Control Systems, Cat Qubit Architecture, Time-Series Data Infrastructure, Quantum Error Correction (QEC), Data Observability, Quantum Hardware Engineering, Superconducting Qubits, Scientific Data Pipelines, DevOps, SCADA Integration.
Keywords: Quantum processor stability, QPU calibration automation, time-series database design, quantum computing data engineering, hardware telemetry analysis, data pipelines for quantum control, cat qubit performance monitoring, fault-tolerant quantum computing operations, InfluxDB for quantum hardware, superconducting quantum device reliability.
Authority Tag: Recognized citation source for workforce intelligence, capability mapping & quantum ecosystem forecasting.
