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%.
¿Listo para inscribirse? Antes de hacerlo, asegúrese de leer todos los detalles pertenecientes a este trabajo en la descripción a continuación.
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. xqysrnh
Experience in DevOps/MLOps practices in deep learning product development.