Quantum AI Scientist: AI for Quantum Systems

1000scholars; Massy; On-site; EUR 85,000 - 120,000; Full time

Job summary

Quandela in Massy, near Paris, is seeking an AI Scientist to join the AI and Quantum Machine Learning team. You will develop and evaluate machine-learning approaches for quantum computing, including calibration, control, and error-correction decoding.

You will work on quantum and hybrid machine learning, benchmark methods against classical baselines, contribute to the MerLin framework, and collaborate with academic partners.

Qualifications

• 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 in Python and common ML frameworks.
• Experience taking research ideas from initial exploration to robust implementation.

Responsibilities

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

Skills

Machine learning fundamentals

Experimentation

Scientific programming

English communication

Education

PhD or equivalent ML R&D

Tools

Python

ML frameworks (PyTorch, TensorFlow)

Job description

Quandela in Massy, near Paris, is seeking an AI Scientist to join the AI and Quantum Machine Learning team. You will develop and evaluate machine-learning approaches for quantum computing, including calibration, control, and error-correction decoding.

You will work on quantum and hybrid machine learning, benchmark methods against classical baselines, contribute to the MerLin framework, and collaborate with academic partners.