Senior Machine Learning Engineer

Overview Join to apply for the Senior Machine Learning Engineer role at Multiverse Computing .

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.

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.

Responsibilities 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, assessing 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.

Apply and enhance techniques to compress Large Language Models based on quantum-inspired technologies as core components of client-facing solutions.

Design and implement pre-training and post-training techniques to train general purpose LLM models and specialize them to specific tasks.

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.

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 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 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 and cloud deployment.

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, both training and inference.

Experience fine-tuning LLMs using SFT, LoRA, or related methods, as well as prompt engineering and model alignment techniques.

Strong problem-solving skills, with the ability to navigate ambiguity and design practical solutions to open-ended user or business needs.

Strong software engineering skills, with proficiency in Python, Docker and Git, and experience building robust, modular, and scalable ML codebases.

Hands-on experience with PyTorch, HuggingFace Transformers, Accelerate, Datasets, DeepSpeed, and related libraries.

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 Artificial Intelligence, Computer Science, Physics, Engineering, or related fields.

Experience handling large datasets, ensuring data quality, diversity, and domain relevance.

Solid understanding of end-to-end ML system design, including data preprocessing, model inference pipelines, evaluation, monitoring, and A / B testing.

Fluent in Spanish.

Perks & Benefits Fixed term contract.

Equal pay guaranteed.

Variable performance bonus.

Signing bonus.

Private health insurance.

Eligibility for educational budget according to internal policy.

Hybrid opportunity.

Flexible working hours.

Language classes and discounted lunch options.

Working in a high paced environment, working on cutting edge technologies.

Career plan. Opportunity to learn and teach.

Progressive Company. Happy people culture.

Equal opportunity employer statement : Multiverse Computing is committed to building an inclusive workplace and welcomes people from all backgrounds, including age, citizenship, ethnic and racial origins, gender identities, individuals with disabilities, marital status, religions and ideologies, and sexual orientations to apply. Come and join our multicultural team.

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