Senior LLM Engineer
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Multiverse Computing is a fast‑growing deep‑tech company founded in 2019 and recognized by CB Insights as one of the 100 most promising AI companies globally. It is the largest quantum software company in the EU, with 250+ employees worldwide, delivering advanced solutions trusted by leading global enterprises across finance, energy, manufacturing, telecom and industrial sectors.
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Our mission is to enable organizations to gain a meaningful competitive edge through cutting‑edge AI and quantum technologies.
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Job Overview
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We are seeking a skilled and experienced LLM Engineer (Senior and Mid level) with a strong background in Large Language Models to join our team. In this role you will lead the design, implementation and improvement of our language models, work closely with cross‑functional teams to integrate these models into our products, and shape the future of LLM and NLP technologies.
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Responsibilities
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- Design and develop new techniques to compress Large Language Models based on quantum‑inspired technologies to solve challenging use cases in various domains.
- Conduct rigorous evaluations and benchmarks of model performance, identify areas for improvement, and fine‑tune and optimise LLMs for enhanced accuracy, robustness and efficiency.
- Assess the strengths and weaknesses of models, propose enhancements, and develop novel solutions to improve performance and efficiency.
- Act as a domain expert in the field of LLMs, understand domain‑specific problems and identify opportunities for quantum AI‑driven innovation.
- Maintain comprehensive documentation of LLM development processes, experiments and results.
- Share knowledge with the team, foster a culture of continuous learning, and guide junior members in their technical growth.
- Participate in code reviews and provide constructive feedback to team members.
- Stay up to date with the latest advancements and emerging trends in LLMs and recommend new tools and technologies as appropriate.
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Required Qualifications
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- Master’s or Ph.D. in Artificial Intelligence, Computer Science, Data Science or related fields.
- Mid: 2+ years of hands‑on experience with designing, training or fine‑tuning transformer models.
- Senior: 5+ years of hands‑on experience with designing, training or fine‑tuning transformer and other deep‑learning models (e.g., computer vision).
- 2+ years of hands‑on experience using LLM and Transformer models, with excellent command of libraries such as HuggingFace Transformers, Accelerate, Datasets, etc.
- Solid mathematical foundations and theoretical understanding of deep learning algorithms and neural networks, both training and inference.
- Excellent problem‑solving, debugging, performance analysis, test design and documentation skills.
- Strong understanding of GPU architectures.
- Excellent programming skills in Python and experience with relevant libraries (PyTorch, HuggingFace, etc.).
- Experience with cloud platforms (ideally AWS), containerisation technologies (Docker) and deploying AI solutions in the cloud.
- Excellent written and verbal communication skills, ability to work collaboratively in a fast‑paced team and communicate complex ideas effectively.
- Previous research publications in deep learning is a plus.
- Fluent in English.
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Preferred Qualifications
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- Proven experience (4+ years) delivering successful AI‑driven projects in an industrial environment.
- Spanish language skills.
- Experience running large‑scale workloads in high‑performance computing (HPC) clusters.
- Experience handling large datasets and ensuring data quality.
- Experience with inference and deployment environments (TensorRT, vLLM, etc.).
- Experience in accuracy evaluation of LLMs (OpenLLM Leaderboard).
- Experience building and evaluating RAG systems.
- Experience in building non‑LLM deep‑learning applications, e.g., computer vision, audio or signal processing.
- Familiarity with AI ethics and responsible AI practices.
- Experience in DevOps/MLOps practices in deep‑learning product development.
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Perks and Benefits
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- Indefinite contract.
- Equal pay guaranteed.
- Variable performance bonus.
- Signing bonus.
- Work visa sponsorship (if applicable).
- Relocation package (if applicable).
- Private health insurance.
- Eligibility for educational budget according to internal policy.
- Hybrid opportunity.
- Flexible working hours.
- Language classes and discounted lunch options.
- High‑performance, collaborative environment operating at pace on cu