Machine Learning Engineer (Service) (Fixed-term contract)

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.

As a Machine Learning Engineer, you will

• 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 techniques.

• Proven experience with agent-based architectures (task decomposition, tool use, reasoning workflows), RAG architectures (retrievers, vector databases, rerankers), and orchestration frameworks (LangGraph, LlamaIndex).

• Strong understanding of end-to-end ML system design, including data sourcing and preparation, training, evaluation, deployment, monitoring, and iteration.

• Experience with system-level evaluation and improvement, including LLM-as-a-judge methods, task-based success metrics, user-focused KPIs, human-in-the-loop validation, and ablations/error analysis to identify and address failure modes.

• Solid experience with data curation and augmentation, including pre-training and post-training pipelines, and experience with synthetic data generation for downstream applications.

• Strong problem-solving and analytical skills, with a system-thinking and customer-oriented mindset to translate complex business needs into technical solutions.

• Proficiency in Python and core ML/data libraries (e.g., PyTorch, HuggingFace, NumPy, Pandas), with strong software engineering practices (Docker, Git, CI/CD, reproducibility, code reviews) and experience building robust, modular, and scalable ML codebases.

• Experience with cloud platforms (ideally AWS).

• Excellent communication skills, with the ability to work collaboratively in a team environment, document and explain design decisions, experimental results, and communicate complex ideas effectively.

• Fluent in English., * Ph.D. in Machine Learning, Computer Science, or a related field with a focus on deep learning, generative AI, or agentic systems.

• Demonstrated experience building and deploying end-to-end Agentic AI or RAG systems in production environments (e.g., with LangGraph, LangChain, LlamaIndex, or custom orchestration frameworks).

• Track record of open-source contributions, technical publications, or community engagement in the ML or generative AI ecosystem.

• Ability to work effectively in cross-functional teams, collaborating with product, customer, and platform stakeholders to deliver practical, high-impact AI solutions.

• Fluent in Spanish.

• 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.

Multiverse is a well-funded and fast-growing deep-tech company founded in 2019. We are one of the few companies working with Quantum Computing and the biggest Quantum Software company in the EU.
We provide hyper-efficient software to companies wanting to gain an edge with quantum computing and artificial intelligence. Our product, Singularity, is a software platform that contains quantum and quantum-inspired algorithms developed and patented through proof-of-concept trials we have been performing for industrial and service clients. We work in finance, energy, manufacturing, cybersecurity and many more industries.

Digital methods usually fail at efficiently tackling these problems. Quantum computing, however, provides us with a powerful toolbox to tackle these complex problems, such as outstanding optimization methods, software for quantum machine learning, and quantum enhanced Monte Carlo algorithms.

Multiverse Computing applies these cutting edge methods to provide software which is customized to your needs, giving companies a chance to derive value from the second quantum revolution.