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About the Role

Job Title: Senior LLM Researcher
Department: Research
Location: San Sebastian, Spain
Employment Type: Full-time
Workplace Type: Hybrid
Seniority Level: Mid-Senior level

Responsibilities

• Design and implement strategies for creating, sourcing, and augmenting datasets tailored for LLM training and fine-tuning.
• Develop scalable pipelines to collect, clean, filter, annotate, and validate large volumes of text data.
• Conduct data audits to ensure quality, diversity, ethical compliance, and bias mitigation.
• Collaborate with ML engineers and researchers to align datasets with training objectives and model evaluation needs.
• Use tools like Active Learning, synthetic data generation, and self-supervised learning to maximize dataset efficiency.
• Leverage human-in-the-loop (HITL) workflows for data labeling and validation where necessary.
• Contribute to building data documentation and metadata standards (e.g., Datasheets for Datasets).
• Keep up to date with research trends in dataset curation, LLM pretraining data, and benchmarking.

Required Qualifications

• Bachelor’s, Master’s, or Ph.D. in Computer Science, AI, Data Science, or a related field.
• 3+ years of experience in data science, machine learning, or related roles, with demonstrated experience in dataset creation for NLP or LLMs.
• In-depth knowledge of the LLM lifecycle: pretraining, fine-tuning, alignment, and evaluation.
• Proficient in Python and data tooling ecosystems (Pandas, NumPy, spaCy, Hugging Face Datasets & Transformers).
• Hands-on experience with text data collection from diverse sources: web scraping, APIs, proprietary corpora, etc.
• Strong understanding of data quality metrics including bias detection, toxicity, and readability.
• Experience working with annotation tools (e.g., Prodigy, Label Studio) and managing annotation teams or workflows.

Preferred Qualifications

• Experience building or contributing to datasets used in LLM pretraining or supervised fine-tuning.
• Familiarity with RLHF workflows and alignment techniques (e.g., preference modeling, reward modeling).
• Exposure to multilingual and low-resource language datasets.
• Contributions to open-source datasets, tools, or publications in dataset-centric research.
• Knowledge of ethical AI, data governance, privacy laws (e.g., GDPR), and responsible data use.

Perks & Benefits

• 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.
• Working in a high-paced environment with cutting-edge technologies.
• Career plan with opportunities to learn and teach.
• Progressive company with a happy people culture.

As an equal opportunity employer, Multiverse Computing is committed to building an inclusive workplace and welcomes applicants from diverse backgrounds.