PWe 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%. /ppbr/ppJoining 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.” /ppbr/ppstrongWe offer /strong /pulliCompetitive annual salary. /liliTwo unique bonuses: signing bonus at incorporation and retention bonus at contract completion. /liliRelocation package (if applicable). /liliFixed-term contract ending in June 2026. /liliHybrid role and flexible working hours. /liliBe part of a fast-scaling Series B company at the forefront of deep tech. /liliEqual pay guaranteed. /liliInternational exposure in a multicultural, cutting-edge environment. /li /ulpbr/ppstrongAs a Machine Learning Engineer, you will /strong /pulliDesign and develop new techniques to compress Large Language Models based on quantum-inspired technologies to solve challenging use cases in various domains. /liliConduct rigorous evaluations and benchmarks of model performance, identifying areas for improvement, and fine-tuning and optimising LLMs for enhanced accuracy, robustness, and efficiency. /liliBuild LLM based applications such as RAG and AI agents. /liliUse your expertise to assess the strengths and weaknesses of models, propose enhancements, and develop novel solutions to improve performance and efficiency. /liliAct as a domain expert in the field of LLMs, understanding domain-specific problems and identifying opportunities for quantum AI-driven innovation. /liliDesign, train and deliver custom deep learning models for our clients /liliWork in diverse areas beyond LLM, e.g., computer vision. /liliMaintain comprehensive documentation of LLM development processes, experiments, and results. /liliShare 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. /liliParticipate in code reviews and provide constructive feedback to team members. /liliStay up to date with the latest advancements and emerging trends in LLMs and recommend new tools and technologies as appropriate. /li /ulpbr/ppstrongRequired Qualifications /strong /pulliBachelor's, Master's or Ph.D. in Artificial Intelligence, Computer Science, Data Science, or related fields. /lili2+ years of hands-on experience with designing, training or fine-tuning deep learning models, preferably working with transformer or computer vision models. /lili2+ year of hands-on experience using transformer models, with excellent command of libraries such as HuggingFace Transformers, Accelerate, Datasets, etc." /liliSolid mathematical foundations and theoretical understanding of deep learning algorithms and neural networks, both training and inference. /liliExcellent problem-solving, debugging, performance analysis, test design, and documentation skills. /liliStrong understanding with the fundamentals of GPU architectures and and LLM hardware/ software infrastructures. /liliExcellent programming skills in Python and experience with relevant libraries (PyTorch, HuggingFace, etc.). /liliExperience with cloud platforms (ideally AWS), containerization technologies (Docker) and with deploying AI solutions in a cloud environment /liliExcellent written and verbal communication skills, with the ability to work collaboratively in a fast-paced team environment and communicate complex ideas effectively. /liliPrevious research publications in deep learning or any tech field is a plus /liliFluent in English /li /ulpbr/ppstrongPreferred Qualifications /strong /pulliExperience running large-scale workloads in high-performance computing (HPC) clusters. /liliExperience in handling large datasets and ensuring data quality. /liliExperience with inference and deployment environments (TensorRT, vLLM, etc.). /liliExperience in accuracy evaluation of LLMs (OpenLLM Leaderboard). /liliExperience building and evaluating RAG systems. /liliExperience in building non-LLM deep learning applications, e.g., computer vision, audio or signal processing. /liliFamiliarity with AI ethics and responsible AI practices. /liliExperience in DevOps/MLOps practices in deep learning product development. /li /ul