Machine Learning Engineer (Service) (Fixed term contract) Technical · Madrid, Barcelona

Job description

Machine Learning Engineer (Service) (Fixed-term contract) We are looking to fill this role immediately and are reviewing applications daily.

Expect a fast, transparent process with quick feedback.

Why join us?

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.” We offer 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.

Job Overview 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 work 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 optimise deep learning models, including large and small language models (LLMs and SMLs), applying fine‑tuning strategies as core components powering our agentic AI and RAG systems.

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 optimisation, 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.

Required Minimum Qualifications 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 ablation/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 (PyTorch, HuggingFace, NumNum, 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.

Preferred Qualifications 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.

About Multiverse Computing Founded in 2019, we are a well‑funded, fast‑growing deep‑tech company with a team of 180 employees worldwide.

Recognised by CB Insights (2023 & 2025) as one of the Top 100 most promising AI companies globally , we are also the largest quantum software company in the EU.

Our flagship products address critical industry needs: CompactifAI – a groundbreaking compression tool for foundational AI models, reducing their size by up to 95% while maintaining accuracy, enabling portability across devices from cloud to mobile and beyond.

Singularity – a quantum and quantum‑inspired optimisation platform used by blue‑chip companies in finance, energy, and manufacturing to solve complex challenges with immediate performance gains.

You’ll be working alongside world‑leading experts in quantum computing and AI, developing solutions that deliver real‑world impact for global clients.

We are committed to an inclusive, ethics‑driven culture that values sustainability, diversity, and collaboration — a place where passionate people can grow and thrive.

Come and join us! Additional Information Department: Technical Role: Machine Learning Engineer Locations: Madrid, Barcelona Employment type: Contract Workplace type: Hybrid Seniority level: Associate Equal Opportunity Employer Statement As an equal‑opportunity employer, Multiverse Computing is committed to building an inclusive workplace.

The company welcomes people from all different backgrounds, including age, citizenship, ethnic and racial origins, gender identities, individuals with disabilities, marital status, religions and ideologies, and sexual orientations to apply.

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Required Skill Profession

Engineering