Quantum-AI Machine Learning Engineer

We are a European deep-tech leader in quantum and AI, backed by major global strategic investors and strong EU support. Our groundbreaking technology is already transforming how AI is deployed worldwide — compressing large language models by up to 95% without losing accuracy and cutting inference costs by 50–80%.

Joining us means working on cutting-edge solutions that make AI faster, greener, and more accessible — and being part of a company often described as a “quantum-AI unicorn in the making.”

We offer

• Competitive annual salary.
• Two unique bonuses: signing bonus at incorporation and retention bonus at contract completion.
• Relocation package (if applicable).
• Fixed-term contract ending in June 2026.
• Hybrid role and flexible working hours.
• Be part of a fast-scaling Series B company at the forefront of deep tech.
• Equal pay guaranteed.
• International exposure in a multicultural, cutting-edge environment.

As a Machine Learning Engineer, you will

• Design and develop new techniques to compress Large Language Models based on quantum-inspired technologies to solve challenging use cases in various domains.
• Conduct rigorous evaluations and benchmarks of model performance, identifying areas for improvement, and fine-tuning and optimising LLMs for enhanced accuracy, robustness, and efficiency.
• Build LLM based applications such as RAG and AI agents.
• Use your expertise to assess the strengths and weaknesses of models, propose enhancements, and develop novel solutions to improve performance and efficiency.
• Act as a domain expert in the field of LLMs, understanding domain-specific problems and identifying opportunities for quantum AI-driven innovation.
• Design, train and deliver custom deep learning models for our clients
• Work in diverse areas beyond LLM, e.g., computer vision.
• Maintain comprehensive documentation of LLM development processes, experiments, and results.
• Share your knowledge and expertise with the team to foster a culture of continuous learning, guiding junior members of the team in their technical growth and helping them develop their skills in LLM development.
• Participate in code reviews and provide constructive feedback to team members.
• Stay up to date with the latest advancements and emerging trends in LLMs and recommend new tools and technologies as appropriate.

Required Qualifications

• Bachelor's, Master's or Ph.D. in Artificial Intelligence, Computer Science, Data Science, or related fields.
• 2+ years of hands-on experience with designing, training or fine-tuning deep learning models, preferably working with transformer or computer vision models.
• 2+ year of hands-on experience using transformer models, with excellent command of libraries such as HuggingFace Transformers, Accelerate, Datasets, etc."
• Solid mathematical foundations and theoretical understanding of deep learning algorithms and neural networks, both training and inference.
• Excellent problem-solving, debugging, performance analysis, test design, and documentation skills.
• Strong understanding with the fundamentals of GPU architectures and and LLM hardware/ software infrastructures.
• Excellent programming skills in Python and experience with relevant libraries (PyTorch, HuggingFace, etc.).
• Experience with cloud platforms (ideally AWS), containerization technologies (Docker) and with deploying AI solutions in a cloud environment
• Excellent written and verbal communication skills, with the ability to work collaboratively in a fast-paced team environment and communicate complex ideas effectively.
• Previous research publications in deep learning or any tech field is a plus
• Fluent in English

Preferred Qualifications

• Experience running large-scale workloads in high-performance computing (HPC) clusters.
• Experience in handling large datasets and ensuring data quality.
• Experience with inference and deployment environments (TensorRT, vLLM, etc.).
• Experience in accuracy evaluation of LLMs (OpenLLM Leaderboard).
• Experience building and evaluating RAG systems.
• Experience in building non-LLM deep learning applications, e.g., computer vision, audio or signal processing.
• Familiarity with AI ethics and responsible AI practices.
• Experience in DevOps/MLOps practices in deep learning product development.