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:
The Workflow team leverages software, AI and automation to improve the efficiency of the R&D processes of Alice & Bob.
As a Senior Applied AI Engineer in this team, you will design, deliver, and maintain high-quality, scalable and robust AI-based solutions to solve complex problems encountered by R&D teams and improve their efficiency. You’ll act as an individual contributor and technical leader to key projects dealing with both simulation and real-life execution of quantum experiments. You’ll work with elite researchers and engineers across Alice & Bob.
You don’t need to be a physics expert to succeed in this position. If you’re curious, motivated, and a fast learner, we’ll teach you everything you need to communicate efficiently with our scientists, and make a positive impact through your work.
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Responsibilities:
- Lead projects from conception to delivery autonomously, navigating ambiguity while meeting timelines.
- Own the design, development, maintenance, and delivery of AI-powered features, tools, or workflows, in partnership with Software Engineers.
- Evaluate, benchmark, and deploy LLMs and agentic AI systems for R&D use cases, selecting appropriate models, architectures, and evaluation methodologies.
- Implement and promote best practices in LLMOps and DevOps (e.g., model versioning, evaluation pipelines, CI/CD, observability, infrastructure as code).
- Lead design and code reviews for AI-related projects, and provide actionable feedback to peers.
- Collaborate efficiently with colleagues of various backgrounds (researchers, engineers, product managers…) to deeply understand their needs and proactively suggest AI-based workflows to make them more efficient.
- Mentor and support more junior engineers in their technical growth and onboarding.
- Participate in the continuous improvement of the team's processes.
Requirements:
- 5+ years of professional software engineering experience, including at least 2 years focused on AI/ML systems in production environments (LLMs, agentic pipelines, RAG, or similar).
- Strong software engineering skills, with a track record of shipping reliable, maintainable code in a team setting (code reviews, CI/CD, testing, observability).
- Hands-on experience evaluating, fine-tuning, and deploying LLMs (prompt engineering, evaluation frameworks, and model selection).
- Familiarity with LLMOps practices: model versioning, evaluation pipelines, monitoring/tracing in production.
- Ability to communicate and work efficiently with colleagues from various backgrounds (researchers, engineers, product managers), and to translate their needs into actionable technical items.
- Professional-level English proficiency, both written and spoken.
Nice to have :
- Experience working in a scientific or R&D-heavy organization.
- Experience with agentic AI frameworks (e.g., LangChain).
- Exposure to workflow automation tools or data pipelines (e.g., Airflow, Prefect).
- Experience with infrastructure-as-code and a cloud platform (e.g., AWS, GCP, Azure).
Recruitment Process:
- Screening call with Doriane (30 min)
- Hiring Manager Interview (45 min)
- Technical Interview with the Team (60 min)
- Leadership Interview (30 min)
- Fit Interview (30 min)
- Reference check
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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 integration of Senior Applied AI Engineers within the quantum sector represents a critical architectural shift toward scientific automation and the Industrialization of R&D processes. As organizations transition from laboratory-scale experiments to scalable, fault-tolerant systems, the structural necessity for AI-driven workflow optimization becomes paramount for managing the high-dimensional data environments inherent in quantum simulation and hardware validation. Market signals from the Quantum Economic Development Consortium and national technology strategies indicate that the primary bottleneck for industrial adoption has migrated from pure physics research to the engineering of reliable, automated software lifecycles. This role type serves as a high-leverage stabilization point within the application enablement layer, ensuring that the convergence of generative AI and quantum information science results in reproducible, deterministic technology roadmaps. By implementing advanced LLMOps and agentic frameworks, this function secures the foundation for long-term enterprise readiness and global competitive differentiation.
The quantum simulation landscape is undergoing a decisive shift from laboratory-scale proof-of-concepts to the integration of high-fidelity computational kernels within global enterprise ecosystems. While hardware development continues across diverse modalities—such as superconducting qubits and neutral atom arrays—the primary bottleneck for industrial adoption has shifted to the algorithmic and workflow layers. The current sector-wide focus lies on bridging classical and quantum capabilities at scale, necessitating a sophisticated management of the software-hardware interface to ensure that hybrid workflows can handle the massive data throughput requirements of production-grade research environments.
Workforce scarcity is particularly acute at the intersection of applied machine learning and quantum information science. As organizations move toward fault-tolerant regimes, the ecosystem requires specialized architects who can navigate the fragmentation of the software stack and the lack of standardized benchmarking protocols. Current industry dynamics, influenced by public-private funding cycles and national security mandates, place a premium on roles that can drive interoperability across disparate quantum cloud platforms. This structural layer of expertise is the primary mechanism for maintaining momentum as the technology transitions through varying Technology Readiness Levels (TRLs).
Integration with existing high-performance computing (HPC) environments remains a high-risk dependency for the sector. The evolution of the value chain depends on the ability to couple AI systems to traditional simulations, enhancing the effective performance of R&D pipelines without disrupting established data sovereignty protocols. Consequently, the availability of senior engineers capable of orchestrating these complex cross-functional dependencies is a primary determinant of whether a commercial organization can successfully transition from exploration to deployment.
The capability architecture for this role type centers on the synchronization of Large Language Model (LLM) orchestration with the rigorous protocols of quantum systems engineering. Mastery of the hardware-agnostic software layer is essential for ensuring that research workflows are optimized for the specific constraints of emerging hardware, such as error correction cycles and gate fidelities. This requires a deep understanding of the integration points between agentic AI frameworks and the underlying quantum compilers that manage hybrid classical-quantum executions.
These capabilities are fundamental to the throughput of technology organizations, as they enable the parallelization of research initiatives alongside the development of scalable cloud architectures. By establishing rigorous LLMOps and evaluation pipelines, this function provides the leverage needed to assess the true value of quantum-enhanced AI before full-scale capital allocation. Furthermore, the ability to manage complex stakeholder landscapes ensures that scientific outputs are reconciled with the practical constraints of enterprise-grade DevOps. Such expertise reduces the iteration friction between abstract research and product delivery, which is critical for long-term interoperability within the emerging quantum-as-a-service market. * Accelerates the deterministic transition from theoretical quantum research to industrial-grade automated R\&D applications
* Mitigates systemic execution risks by synchronizing long-term AI research cycles with near-term technology roadmaps
* Facilitates the integration of agentic AI systems into standardized cloud and high-performance computing infrastructures
* Strengthens the reliability of organizational technology strategies through the implementation of rigorous algorithmic benchmarking
* Reduces iteration friction between fundamental physics breakthroughs and the deployment of scalable software architectures
* Optimizes the allocation of specialized technical talent across research, development, and strategic liaison portfolios
* Enhances the stability of the quantum software value chain by providing predictable requirement frameworks for external partners
* Supports the scaling of simulation capabilities by managing the complex dependencies of hybrid quantum-classical workflows
* Improves the transparency of technology readiness level progression for stakeholders in the investment and policy sectors
* Enables the structural reproducibility of quantum experiments through the standardization of AI-driven architectural implementation
* Protects high-capital research and development investments by ensuring alignment between scientific discovery and commercial scalability
* Orchestrates the convergence of academic research pathways with the practical demands of global enterprise-ready servicesIndustry Tags: Quantum AI Integration, LLMOps, Fault-Tolerant Computing, R&D Automation, Hybrid Quantum-Classical, Agentic AI Frameworks, Technology Translation, Deep Tech Strategy, Software Development Life Cycle
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