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 250+ brilliant minds from over 35 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!
About the role
We are building a modern, AI-ready Data Platform on Google Cloud Platform.
As a Senior MLOps / LLMOps Engineer, you will help turn ML and AI experiments into reliable, production-ready solutions. You will work closely with researchers, data scientists and engineers to build the infrastructure and practices needed to train, deploy and operate models at scale.
You will also play an important role in helping teams adopt AI technologies and best practices across the company.
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Responsibilities
- Build and maintain MLOps / LLMOps infrastructure on GCP, including ML pipelines, model deployment and monitoring.
- Help researchers and data scientists bring models from experimentation to production.
- Design reliable and scalable solutions for both traditional ML and GenAI / LLM use cases.
- Ensure models and experiments are reproducible, observable and maintainable.
- Advise teams on ML/AI architecture, tooling and best practices.
- Share knowledge and help teams make effective use of GCP AI services.
- Work closely with Data Engineering and DevOps teams on data, infrastructure and CI/CD.
- Contribute to the evolution of the ML/AI platform as the company's AI needs grow.
Requirements
- 7+ years of experience in ML Engineering, MLOps, Software Engineering or a related field.
- Strong Python skills and experience running ML systems in production.
- Solid experience with MLOps, including pipelines, deployment and monitoring.
- Experience with GCP / Vertex AI is a strong plus.
- Experience with Docker and Kubernetes/GKE.
- Familiarity with modern ML frameworks such as PyTorch, TensorFlow or scikit-learn.
- Experience with LLMs / GenAI.
- Strong analytical and communication skills, with the ability to work with both technical and research teams.
- Fluent in English.
Recruitment Process
- Screening call with Doriane (30 min)
- Hiring Manager interview (45 min)
- Technical onsite Interview - (90min)
- Leadership Interview (30 min)
- Fit Interview (30 min)
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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
The emergence of Senior MLOps Engineers within quantum hardware research and manufacturing organizations reflects a vital structural pivot toward AI-assisted design, calibration, and error mitigation in deep-tech environments. As quantum hardware scales toward fault tolerance, the sheer volume of diagnostic data and physical control variables exceeds classical manual tuning capacity, making production-grade machine learning pipelines necessary for hardware operational stability. This role bridges experimental physics workflows and industrial cloud infrastructures, transforming ad-hoc predictive models into robust software layers that govern system-level execution. By establishing automated continuous integration and training pipelines, this engineering capability mitigates execution risks associated with research-to-production bottlenecks. Current industry focus lies on bridging classical and quantum capabilities at scale, where machine learning operations directly support system availability, qubit fidelity tracking, and automated recalibration protocols across scaling platforms.
Within the deep-tech and quantum technology value chain, MLOps functions act as an essential operational substrate connecting physical hardware engineering with scalable software stack enablement. As quantum computing organizations move from early laboratory proofs-of-concept toward commercial scaling, the complexity of managing physical control systems, error mitigation models, and generative tooling increases exponentially.
Macro constraints in this sector stem from the integration gap between experimental research code and production cloud infrastructure. Academic models developed during exploratory phases frequently suffer from technical debt, lack of observability, and non-reproducible environments, creating severe technology readiness level mismatches when scaled across multi-qubit architectures.
Furthermore, hybrid compute fabrics require continuous, automated calibration loops where classical machine learning algorithms optimize physical hardware dynamics in real time. Organizations like Alice & Bob leverage cloud-native AI data platforms to accelerate these diagnostic cycles and support complex modeling environments.
Sector-wide efforts continue to address talent and integration challenges in quantum systems by embedding structured software engineering methodologies into physics-heavy R&D teams. Standardizing MLOps tooling ensures that internal predictive capabilities remain resilient against vendor fragmentation, reducing structural friction across cloud orchestration and automated hardware control workflows.
The technical architecture for MLOps roles in quantum engineering spans cloud-native data platforms, automated orchestration frameworks, and containerized deployment infrastructure. Core capabilities center on constructing reproducible machine learning pipelines, scalable model registry patterns, and real-time inference endpoints capable of managing high-throughput telemetry data from physical lab systems. Expertise in cloud AI services, container orchestration via Kubernetes, and Infrastructure-as-Code enables the conversion of complex scientific scripts into maintainable microservices. Advanced generative AI and large language model enablement layers further facilitate knowledge management and automated code generation across cross-functional research teams. These competencies ensure operational leverage, allowing hardware developers and quantum algorithmists to iterate rapidly without risking software platform degradation or deployment downtime. - Accelerates the operational transition of experimental physics models into production-grade infrastructure
- Minimizes hardware recalibration latency through automated continuous training and deployment pipelines
- Enhances research velocity by establishing reproducible cloud-native machine learning environments
- Mitigates technical risk by enforcing standardized software architecture across cross-functional teams
- Optimizes data throughput between lab control instrumentation and centralized cloud analytics platforms
- Facilitates organizational AI adoption through structured model governance and observability frameworks
- Strengthens system stability by automating anomaly detection across physical quantum hardware metrics
- Reduces iteration cycles between algorithm design and real-world system execution
- Protects intellectual property assets by maintaining centralized and audited ML deployment registries
- Supports fault-tolerant engineering roadmaps through scalable data engineering and infrastructure enablement
- Unifies heterogeneous telemetry streams to streamline diagnostic and error mitigation research
- Drives operational efficiency across internal developer platforms supporting deep-tech research teamsIndustry Tags: Quantum Computing, MLOps, LLMOps, Cloud Infrastructure, Machine Learning Engineering, GCP, Vertex AI, Kubernetes, Fault-Tolerant Architecture, Automated Calibration, Deep Tech Enablement
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