Senior Machine Learning Engineer (Product)

Michael Page; Zaragoza; Híbrido; EUR 30.000 - 50.000; Jornada completa

Descripción de la vacante

A leading tech recruitment firm seeks a skilled professional for a hybrid role in Zaragoza. The position involves designing and implementing strategies for creating and sourcing datasets for LLM training. Ideal candidates are fluent in English and Spanish, possess a strong background in developing scalable ML pipelines, and can mentor colleagues. The role offers a competitive salary, bonuses, flexible hours, and the possibility of a permanent contract.

Servicios

Competitive salary

Signing bonus

Retention bonus

Flexible working hours

Focus on technology innovation

Formación

• Strong experience in designing strategies for datasets for LLM training.
• Expertise in developing scalable pipelines for data collection and validation.
• Proven ability to collaborate with ML engineers and software developers.

Responsabilidades

• Implement strategies for creating and sourcing LLM training datasets.
• Develop scalable data pipelines for cleaning and validating text data.
• Collaborate with engineers to prepare and evaluate LLMs.

Conocimientos

Dataset creation and sourcing

ML pipeline development

English fluency

Spanish fluency

Distributed compute optimization

Descripción del empleo

• European deep-tech leader in quantum and AI
• Hybrid Role (Zaragoza city). Fluency in English and Spanish.

European deep-tech leader in quantum and AI

• 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.

• Competitive salary + Two unique bonus (signing and retention)
• Fixed-term contract (possibility of becoming permanent)
• Hybrid role and flexible working hours.
• Chance to be part of organization with a focus on technology innovation.