AI scientist for Quantum Computing (Massy)

About Quandela:

Quandela is a European deeptech company building modular, scalable and energy-efficient spin-photonic quantum computers.

We develop the full quantum computing stack, from semiconductor quantum emitters and photonic processors to control systems, software and applications. Our platforms are available through the cloud and as on-premises systems.

Our ambition is to build fault-tolerant quantum computers capable of solving problems beyond the reach of classical computation.

The role:

We are looking for an AI Scientist to join Quandela’s AI and Quantum Machine Learning team in Massy (near Paris)

You will develop and evaluate machine-learning approaches for quantum computing, covering both:

• AI for quantum systems , including calibration, control, decoding for quantum error correction;
• quantum and hybrid machine learning , including rigorous comparisons with state-of-the-art classical methods, using MerLin, Quandela's framework for quantum machine learning.

You will work on research with colleagues across AI, quantum algorithms, software, product and hardware.

The Massy team is a central part of Quandela’s AI activities. This role therefore offers significant ownership and the opportunity to help shape and grow the team over time.

What You'll Do:

• Design, implement and train machine-learning and quantum machine-learning models.
• Develop AI methods to improve the calibration, control and performance of quantum systems.
• Benchmark classical, quantum and hybrid methods using meaningful baselines and evaluation metrics.
• Contribute actively to the development of the MerLin framework.
• Contribute to publications and collaborations with academic partners, including research ecosystem.
• Present your work internally and at scientific conferences and technical events.
• Help define Quandela’s future AI research directions and mentor junior researchers as the team grows.

What we are looking for:

We are looking for an AI scientist, or an experienced industry practitioner with an equivalent background, who is eager to apply state-of-the-art classical machine-learning methods to scientific and engineering challenges in quantum computing. The role is primarily focused on using AI to improve quantum technologies, while also providing opportunities to develop and investigate quantum and hybrid machine-learning models.

• PhD + 3 years of experience or equivalent industrial R&D experience in machine learning.
• Strong foundations in modern machine learning, experimental methodology and model evaluation.
• Excellent scientific programming skills, particularly in Python and common machine-learning frameworks.
• Experience taking research ideas from initial exploration to robust implementation.
• Interest in working on both longer term research and concrete product problems.
• Strong communication skills and the ability to work autonomously within a focused, multidisciplinary team.
• Professional fluency in English.

Bonus Points

• Previous experience applying AI to physical systems, scientific computing, hardware control or experimental data.
• Knowledge of quantum computing, quantum machine learning or photonic quantum technologies.
• Experience mentoring junior researchers or helping define technical research directions.
• Competitive salary, flexible based on your experience and expectations
• Profit-sharing & company savings plan
• 100% health coverage (Alan)
• 50% transport reimbursement or sustainable mobility bonus
• Meal vouchers (Swile), Gymlib fitness support
• Childcare assistance & referral bonuses (€1,000–€2,000)

QUANTUM ROLE CONTEXT | Appended by Quantum.Jobs v2

Role context:

This role type exists to apply advanced data-driven algorithms to complex computational and physical challenges within quantum hardware and software environments. Positioned at the intersection of machine learning research, software engineering, and experimental physics, the position serves as an essential link for optimizing system performance. Professionals in this capacity develop empirical models to refine system parameters, evaluate hybrid computational frameworks, and enhance hardware stability. By bridging algorithmic methodology with physical hardware operations, these specialists help multidisciplinary engineering teams translate theoretical research into structured computational workflows.

Quantum ecosystem relevance:

This role type contributes to the quantum ecosystem by integrating machine learning techniques into hardware control, system calibration, and algorithmic evaluation. As quantum hardware scales, automated optimization methods become necessary to manage system noise, improve error decoding, and refine experimental control logic. Additionally, evaluating hybrid quantum-classical models against standard classical baselines provides objective benchmarks for computational performance. By developing standardized frameworks for algorithmic testing and physical optimization, specialists in this field help advance the reliability and software capabilities required for practical quantum computing deployment.

Capability signals:

- Advanced expertise in machine learning methodology and statistical algorithm evaluation

- Demonstrated proficiency in scientific programming languages and modern modeling frameworks

- Track record of applying computational algorithms to physical or experimental systems

- Ability to collaborate across multidisciplinary software, hardware, and research teams

- Experience translating theoretical algorithmic concepts into functional software tools