Senior Machine Learning Engineer (Spain)

At RemoteStar, we're currently hiring for one of our client based in Spain.

• Master’s, or Ph.D. in Computer Science, AI, Data Science, Physics, Math, or a related field. Or equivalent industry experience.
• 4+ years of experience in data science, machine learning, or related roles, with demonstrated experience with NLP or LLMs.
• In-depth knowledge of large foundational model architectures (language and multimodal models) and their lifecycle: training, fine-tuning, alignment, and evaluation.
• Proficient in Python and data tooling ecosystems (Pandas, NumPy, Hugging Face Datasets & Transformers libraries).
• 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 in large shared distributed computing environments, familiarity with relevant tools for hardware optimization (vLLM, TensorRT, NeMo, etc.).
• Experience with version control (git), unit testing, and other fundamental aspects of software development.
• Effective communication and interpersonal abilities.

• Experience building or contributing to datasets used in LLM pretraining or supervised fine-tuning.
• Experience building foundational LLMs from the ground up
• Familiarity with alignment techniques (e.g., reinforcement learning, 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.
• Familiarity with the software development lifecycle and agile methodologies

• 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, ensuring quality, ethical compliance, etc.
• Collaborate with ML engineers, researchers, and software engineers to achieve ambitious goals in the preparation of LLMs and complementary work (preparing datasets, model evaluation, model serving, etc.).
• Develop and integrate new routines for modifying and enhancing LLMs, and extending their functionality.
• Make effective use of distributed compute resources and clusters (GPU’s), identify opportunities for further optimization.
• End-to-end preparation of compressed and specialized LLMs for use in production.
• Keep up to date with research trends in LLM foundation models, dataset curation, LLM pretraining data, and benchmarking.
• Contribute to building documentation, development standards, and a healthy shared code base.
• Mentor other engineers and provide knowledge sharing of cutting-edge techniques.