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 200+ brilliant minds from over 31 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!
Building a fault-tolerant quantum computer by 2030 requires every support function to operate at the same level of ambition as the lab. Reporting to the Chief of Staff, you will be embedded across Finance, Legal, HR, Operations, and Business to understand how each team works, identify which recurring tasks can be encoded as a Claude skill, and build them.
The output is concrete: a growing library of production-ready Claude skills, each one owned and used by the team it was built for.
Alice & Bob grew from 5 to 250+ people in six years and is one of the fastest-growing deep-tech startups in Paris.
AI integration is not a side project here it is a company-wide strategic priority, and this role sits at the center of it, with direct visibility from leadership.
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Responsabilities:
- Conduct structured process interviews with leads across Finance, Legal, HR, Operations, and Business / Revenue to map recurring, high-cognitive-load tasks
- Identify which tasks are suitable for Claude skill encoding — well-defined inputs, predictable structure, clear success criteria
- Write, test, and iterate Claude skills: system prompts, instructions, behavioral rules, few-shot examples, output formats, and edge case handling
- Validate each skill directly with end users until it performs reliably for non-technical team members
- Maintain a documented skill library with scope, usage guide, known limitations, and update log for each skill
- Build and share a reusable methodology for skill writing that other teams can apply independently after the internship
Requirements:
- Final-year student (M2 or gap year) from a Tier 1 business or engineering school
- Strong working knowledge of Claude and prompt engineering — you understand how LLMs reason, where they fail, and how instruction design affects output quality
- Ability to write precise, structured specifications: unambiguous instructions, well-chosen examples, explicit edge case handling
- Analytical approach to process decomposition — able to turn a vague workflow description into a defined task with clear inputs and outputs
- Fluent English — skills will be written and tested
- French is a plus
- Comfortable operating with autonomy; the role requires self-direction across five departments simultaneously
- Prior experience building Claude skills, custom GPT instructions, or equivalent structured prompt systems that were actually used by others
- Exposure to one or more of the target functions: Finance, Legal, HR, Operations, or B2B Sales — enough to recognize where the real friction is
- Genuine interest in quantum computing and the deep-tech space
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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
The emergence of specialized roles focused on AI transformation within the quantum computing sector signifies a critical shift toward operational maturity in deep-tech organizations. As the industry moves from laboratory proof-of-concepts to the complex requirements of fault-tolerant systems, the structural necessity for high-leverage internal tooling becomes paramount. This role type serves as a foundational enablement layer, ensuring that the velocity of scientific discovery is matched by the efficiency of organizational support functions. By converting high-cognitive-load processes into deterministic AI-driven workflows, this function secures the internal stability required to navigate long-term technology roadmaps. Market signals from major consultancy reports and national technology strategies highlight that such internal digital maturity is essential for mitigating the systemic risks of rapid scaling in workforce-constrained environments.
The quantum ecosystem is currently navigating a decisive transition phase where the primary bottleneck for industrial success has expanded beyond hardware milestones to include the scalability of organizational infrastructure. In this context, the role of AI-driven transformation sits within the enablement layer of the value chain, acting as a force multiplier for deep-tech firms. While much of the sector's focus remains on qubit coherence and error correction, the ability to maintain institutional knowledge and process efficiency during rapid headcount growth is a significant differentiator for market leaders. Current industry focus lies on bridging classical and quantum capabilities at scale, which requires a highly agile administrative and operational backbone.
Workforce scarcity is not limited to quantum physicists; there is an acute need for professionals who can navigate the interface between advanced Large Language Models and specialized domain workflows in Finance, Legal, and Operations. As deep-tech organizations move through varying Technology Readiness Levels, the lack of standardized operational protocols can lead to significant iteration friction. Sector-wide efforts continue to address these integration challenges by embedding generative AI capabilities directly into the corporate fabric. This structural layer of expertise is the primary mechanism for maintaining momentum as companies transition from early-stage research to global enterprise-ready services.
Furthermore, the evolution of the quantum value chain depends on the ability of support functions to handle the increasing complexity of international regulatory landscapes and complex supply chain dependencies. Ongoing ecosystem initiatives aim to accelerate readiness for practical applications by optimizing the internal throughput of the organizations building them. Consequently, the availability of experts capable of orchestrating these complex cross-functional AI integrations is a primary determinant of whether a commercial organization can successfully sustain the capital-intensive path toward fault tolerance.
The capability architecture for this role type centers on the synchronization of advanced prompt engineering with the rigorous protocols of business process decomposition. Mastery of model-specific reasoning patterns and instruction design is essential for ensuring that automated "skills" are optimized for the specific constraints of non-technical end users. This requires a deep understanding of the integration points between unstructured workflow inputs and the structured outputs required by enterprise-grade systems. Such expertise is fundamental to the stability of the organization, as it enables the parallelization of administrative scaling alongside scientific development. By establishing documented libraries of AI capabilities, this function provides the leverage needed to maintain operational quality without a linear increase in overhead. This reduces the friction between internal policy and external delivery, which is critical for long-term interoperability within the emerging quantum-as-a-service market. - Accelerates the deterministic transition from manual support workflows to AI-enhanced operational models
- Mitigates systemic scaling risks by encoding institutional knowledge into reusable digital assets
- Facilitates the integration of advanced generative AI models into standardized deep-tech business processes
- Strengthens the reliability of organizational support functions through the implementation of rigorous prompt benchmarking
- Reduces iteration friction between cross-functional departments by standardizing high-cognitive-load task execution
- Optimizes the allocation of specialized human capital by automating recurring administrative dependencies
- Enhances the stability of the deep-tech value chain by providing predictable internal requirement frameworks
- Supports the scaling of organizational capabilities by managing the complex dependencies of AI-human collaboration
- Improves the transparency of operational readiness for stakeholders in the investment and policy sectors
- Enables the structural reproducibility of business processes through the standardization of AI-driven protocols
- Protects high-capital research and development focus by ensuring administrative functions operate with lab-grade efficiency
- Orchestrates the convergence of emerging AI tooling with the practical demands of global quantum enterprisesIndustry Tags: AI Transformation, Quantum Ecosystem Enablement, Prompt Engineering, Deep Tech Operations, Process Automation, Generative AI Integration, Workforce Maturity, Scaling Strategy, Digital Transformation
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