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 Performance Optimization team is at the heart of our mission: using Machine Learning to dramatically improve the quality, speed, and reliability of the quantum chip lifecycle.
As a Senior Machine Learning Engineer, you will own the architectural blueprint and technical direction of key components in our ML stack. This is a high-autonomy role where you’ll set the standards for how we build, scale, and deploy.
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Responsibilities
- Architecture & Deployment: Lead the design and implementation of complex ML workflows. You will architect solutions that are not just accurate, but scalable, maintainable, and observable in production.
- Cross-Functional Leadership: Proactively collaborate with quantum physicists to identify bottlenecks in the chip lifecycle. You will translate high-level physical constraints into precise algorithmic requirements and drive the solution to completion.
- Technical Roadmap: Identify gaps in the ML stack and proactively propose, scope, and prioritize multi-quarter improvements. You will own the roadmap for one or more core optimization components.
- Mentorship & Standards: Conduct code reviews and guide junior engineers in software design patterns. You will play a key role in defining the "gold standard" for engineering within the optimization team.
- Engineering Influence: Contribute to engineering standards beyond the immediate team. Participate in cross-team design reviews, define reusable patterns, and help shape how ML is built across the organization.
Requirements
- 5+ years of industry experience in Machine Learning Engineering or Software Engineering with a strong ML focus, or a PhD + 3 years in the industry.
- Ownership Track Record: Demonstrated experience independently owning an ML project end-to-end, from ambiguous problem definition through production deployment and iteration.
- Technical Proficiency: Advanced expertise in Python and the modern ML stack (PyTorch/JAX). Proven experience building and maintaining production-grade software (not just notebooks).
- System Knowledge: Experience with MLOps tools, distributed training, or cloud infrastructure.
- Mathematical Fluency: Strong grasp of linear algebra and optimization, with the ability to discuss technical trade-offs with research scientists.
Nice to Have
- Deep Tech Experience: Experience working in hardware-constrained environments (robotics, semiconductors, physics) or with scientific computing.
- Research Impact: Lead authorship at top-tier ML conferences, or a background in Physics/Quantum mechanics.
Recruitment Process
- Screening Call with Grace, Talent Acquisition Specialist (30 min)
- Hiring Manager Interview with Etienne (45 min)
- Technical Interview with the Team (90 min)
- Leadership Team Interview (30 min)
- Fit Interview (45 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 machine learning into the quantum hardware lifecycle represents a critical shift from manual calibration to automated, data-driven optimization of physical systems. As quantum processors transition toward hundreds of logical qubits, the structural necessity for senior engineers who can bridge the gap between abstract ML workflows and hardware-constrained physical environments becomes paramount for achieving fault tolerance. This role type serves as a primary mechanism for reducing the high-dimensional noise floor and improving the fidelity of superconducting platforms through autonomous error correction strategies. By establishing scalable software architectures for chip characterization, this function secures the stability of the development stack against the systemic risks of hardware fragmentation and technical debt. Market signals from global technology reports highlight that such cross-disciplinary expertise is essential for navigating the transition from laboratory prototypes to practical, production-grade quantum machines.
The quantum software ecosystem is undergoing a decisive move away from isolated laboratory experiments toward integrated systems where machine learning serves as a fundamental layer for performance optimization and error mitigation. While hardware modalities vary, the primary bottleneck for the sector has shifted to the software-hardware interface, specifically regarding the reproducibility of high-fidelity qubit operations. Current industry dynamics, influenced by national security mandates and massive public-private funding cycles, place a premium on roles that can drive interoperability between deep-tech hardware and standardized MLOps infrastructures.
Workforce scarcity is particularly acute at the intersection of quantum information science and enterprise-grade software engineering. As organizations move toward fault-tolerant architectures, the ecosystem requires specialized architects capable of translating high-level physical constraints into precise algorithmic requirements without disrupting the research-to-production pipeline. This structural layer of expertise is essential for maintaining momentum as the technology transitions through varying Technology Readiness Levels (TRLs), ensuring that the underlying chip lifecycle remains observable and maintainable at scale.
Integration with existing high-performance computing (HPC) and cloud environments remains a high-risk dependency for the sector. The evolution of the quantum value chain depends on the ability to manage complex hybrid classical-quantum workflows, where classical ML models are utilized to govern and optimize quantum states in real-time. Consequently, the availability of senior engineers who can orchestrate these cross-functional dependencies is a primary determinant of whether a commercial organization can successfully transition from noise-limited exploration to deterministic quantum advantage.
The capability architecture for this role type centers on the synchronization of advanced optimization theory with the rigorous protocols of production-grade systems engineering. Mastery of the hardware-agnostic software layer is essential for ensuring that ML models are optimized for the specific constraints of superconducting circuits, such as limited coherence times and autonomous error correction thresholds. This requires a deep understanding of the integration points between modern ML stacks, like PyTorch or JAX, and the low-level controllers that manage quantum hardware. Such capabilities are fundamental to the throughput of technology organizations, as they enable the parallelization of hardware development alongside the scaling of automated validation frameworks. By establishing "gold standards" for code quality and observability, this function provides the leverage needed to assess the true reliability of quantum architectures before full-scale capital allocation. These interface points reduce the iteration friction between fundamental physics breakthroughs and the deployment of scalable services, which is critical for long-term interoperability within the emerging quantum-as-a-service market. - Accelerates the deterministic transition from noise-limited hardware prototypes to fault-tolerant quantum computing systems
- Mitigates systemic execution risks by implementing scalable and observable machine learning workflows for chip characterization
- Facilitates the integration of autonomous error correction protocols into standardized quantum hardware development lifecycles
- Strengthens the reliability of technology roadmaps through the implementation of rigorous algorithmic benchmarking and optimization
- Reduces iteration friction between experimental physics breakthroughs and the deployment of production-grade software stacks
- Optimizes the allocation of specialized technical talent by establishing high-authority engineering standards across research teams
- Enhances the stability of the quantum value chain by providing predictable architectural frameworks for hybrid classical-quantum systems
- Supports the scaling of qubit fidelity by managing the complex multidimensional optimization of superconducting physical states
- Improves the transparency of technology readiness level progression for stakeholders in the investment and deep-tech sectors
- Enables the structural reproducibility of quantum experiments through the standardization of MLOps for hardware calibration
- Protects high-capital research and development investments by ensuring alignment between scientific discovery and industrial scalability
- Orchestrates the convergence of machine learning engineering with the practical demands of universal quantum computer developmentIndustry Tags: Quantum Machine Learning, Fault Tolerant Computing, Superconducting Qubits, MLOps, Performance Optimization, Deep Tech Engineering, Quantum Error Correction, Hardware Lifecycle Management, Python Scientific Computing
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