Machine Learning Engineer; Service

We are looking to fill this role immediately and are reviewing applications daily. Expect a fast, transparent process with quick feedback.

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.”

• Competitive annual salary starting from €55,000, based on experience and qualifications.
• 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.

We are seeking a skilled and experienced Machine Learning Engineer with a strong technical background in Generative AI to join our team. In this role you will have the opportunity to leverage cutting‑edge quantum and AI technologies to lead the design, implementation, and deployment in production environments of Generative AI systems, as well as working closely with cross‑functional teams to integrate these models into our products.

You will have the opportunity to work on challenging projects, contribute to cutting‑edge research, and shape the future of Generative AI and LLM technologies.

• Build end‑to‑end Agentic AI systems and RAG pipelines that combine retrieval, reasoning, and planning capabilities, integrating them into customer‑facing solutions across cloud and edge environments.
• Design, train, and optimize deep learning models, including Large and Small Language Models (LLMs and SMLs), applying fine‑tuning strategies, as core components that power our Agentic AI and RAG systems of client‑facing solutions.
• Drive end‑to‑end ML system design, encompassing data sourcing and curation, training, evaluation, deployment, monitoring, and continuous iteration — not just model development.
• Develop and refine rigorous evaluation frameworks that go beyond model benchmarks to assess system performance on task success, key KPIs, and user‑level outcomes across diverse domains.
• Fine‑tune and adapt language models using methods such as SFT, prompt engineering, and reinforcement or preference optimization, tailoring them to domain‑specific tasks and real‑world constraints.
• Design and implement strategies for data curation and augmentation, including pre‑training and post‑training data pipelines, synthetic data generation, and task‑specific dataset creation tailored to downstream applications.
• Maintain high engineering standards, including clear documentation, reproducible experiments, robust version control, and well‑structured ML pipelines.
• Contribute to team learning and mentorship, guiding junior engineers and fostering best practices in ML system design, training workflows, evaluation, and integration with production systems.
• Participate in code reviews, offering thoughtful, constructive feedback to maintain code quality, readability, and consistency.
• Stay up‑to‑date with emerging trends in ML and Generative AI, and proactively recommend tools, frameworks, and methods to enhance our technology stack.

• Master's or Ph.D. in Computer Science, Machine Learning, Data Science, Physics, Engineering, or related technical fields, with relevant industry experience.
• 3+ years of hands‑on experience building, training, and deploying machine learning systems in production, including at least 2 years focused on Generative AI, RAG systems, or Agentic AI.
• Proven experience designing, training, and fine‑tuning deep learning models from scratch (e.g., LLMs, computer vision, transformer‑based), including SFT, prompt engineering, and model alignment…