Senior Machine Learning Engineer

Multiverse is a well-funded, fast-growing deep-tech company founded in 2019. We are the largest quantum software company in the EU and have been recognized by CB Insights (2023 and 2025) as one of the 100 most promising AI companies in the world.
With 180+ employees and growing, our team is fully multicultural and international. We deliver hyper-efficient software for companies seeking a competitive edge through quantum computing and artificial intelligence.

Our flagship products, CompactifAI and Singularity, address critical needs across various industries:

- CompactifAI is a groundbreaking compression tool for foundational AI models based on Tensor Networks. It enables the compression of large AI systems—such as language models—to make them significantly more efficient and portable.
- Singularity is a quantum
- and quantum-inspired optimization platform used by blue-chip companies to solve complex problems in finance, energy, manufacturing, and beyond. It integrates seamlessly with existing systems and delivers immediate performance gains on classical and quantum hardware.

You’ll be working alongside world-leading experts to develop solutions that tackle real-world challenges. We’re looking for passionate individuals eager to grow in an ethics-driven environment that values sustainability and diversity.
We’re committed to building a truly inclusive culture—come and join us.

We are seeking a skilled and experienced **Senior Machine Learning Engineer**with a strong technical background in Generative AI and Large Language Models 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 our language models and 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.

**As a Senior Machine Learning Engineer, you will**
- Design and implement end-to-end Generative AI systems, including multi-component RAG pipelines and autonomous AI agents, integrating retrieval, orchestration, tool usage, and memory to solve complex, real-world problems across diverse industries.
- Develop and refine rigorous evaluation frameworks that reflect real-world performance, going beyond model benchmarks to assess task success, reasoning capabilities, factual consistency, reliability, and user success metrics across diverse use cases.
- Drive end-to-end ML system design, encompassing data sourcing and curation, training, evaluation, deployment, monitoring, and continuous iteration — not just model development.
- Design and implement pre-training and post-training techniques to train general purpose LLM models and specialize them to specific tasks.
- Evaluate and improve model and system performance through targeted evaluation, error analysis, ablation studies, and exploration of new methods to increase robustness, efficiency, and generalizability.
- Fine-tune and adapt LLMs using techniques like SFT and prompt engineering to tailor them for specific client use cases and domain constraints.
- 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 sharing expertise in LLM development, evaluation, and deployment best practices.
- Participate in code reviews, offering thoughtful, constructive feedback to maintain code quality, readability, and consistency.
- Stay up-to-date with emerging trends in Generative AI and LLMs, and proactively recommend tools, frameworks, and methods to enhance our technology stack.

**Required minimum Qualifications**
- Master's or Ph.D. in Artificial Intelligence, Computer Science, Physics, Engineering, or related fields, with 2+ years of relevant industry experience.
- 4+ years of hands-on experience with designing, training or fine-tuning deep learning models, preferably transformers.
- 2+ years of hands-on experience building and deploying machine learning systems in production, including 1+ years focused on Generative AI (e.g., RAG pipelines, LLMs, or AI agents) and deployment in cloud-based production environments.
- Proven experience with RAG architectures, including retrievers, vector databases (e.g., Qdrant, Chroma), rerankers, and orchestration frameworks (e.g. LlamaIndex).
- Proven experience with system-level evaluation techniques, including LLM-as-a-judge methods, task-based success metrics, hallucination detection, and human-in-the-loop validation.
- Proven experience and solid theoretical understanding of deep learning models and neural networks (e.g., computer vision, LLMs, etc.), both training and inference.
- Experience fine-tuning LLMs using SFT, LoRA, or related