AI Research Engineer - Pre training

Jobgether; Madrid; Presencial; EUR 70.000 - 90.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive compensation package

Remote-friendly work environment

Access to high-performance computing infrastructure

Professional growth opportunities

Descripción de la vacante

A leading AI research firm in Spain seeks an experienced AI Research Engineer to advance large-scale pre-training systems. You will work directly with cutting-edge GPU architectures to design training pipelines and improve model performance. Ideal candidates have a PhD in Computer Science or related fields, hands-on experience in LLM pre-training, and strong collaboration skills. Enjoy a competitive compensation package and a remote-friendly work environment focused on frontier AI research.

Formación

• PhD or strong academic/research background in Computer Science, Machine Learning, NLP, or related fields.
• Hands-on experience with large-scale LLM pre-training on distributed GPU infrastructure.
• Strong understanding of transformer architectures and advanced model design techniques.

Responsabilidades

• Conduct large-scale pre-training of AI models on distributed GPU clusters.
• Design, prototype, and optimize novel model architectures.
• Run experiments, analyze results, and refine methodologies.

Conocimientos

Large-scale LLM pre-training

Transformer architectures

PyTorch

Hugging Face ecosystem

Debugging and optimizing model performance

Collaboration in research-driven environments

Educación

PhD or strong academic/research background in Computer Science, Machine Learning, NLP

Herramientas

Distributed training frameworks

Descripción del empleo

AI Research Engineer – Pre Training (Spain)

We are currently looking for an AI Research Engineer – Pre Training in Spain. This role sits at the core of next-generation AI model development, focusing on advancing large-scale pre-training systems that power state-of-the-art intelligence. You will work on cutting‑edge architectures spanning small, large, and multimodal models, directly influencing model capability, efficiency, and scalability. Operating in a highly research‑driven and distributed engineering environment, you will help push the boundaries of what modern AI systems can achieve. The position combines deep scientific exploration with hands‑on engineering on massive GPU clusters. You will design and optimize training pipelines that run across thousands of NVIDIA GPUs, ensuring performance at scale. This is an opportunity to contribute to foundational AI breakthroughs while collaborating with world‑class researchers and engineers in a fast‑paced, innovation‑focused setting.

Accountabilities

• Conduct large‑scale pre‑training of AI models on distributed GPU clusters, ensuring scalability, stability, and performance
• Design, prototype, and optimize novel model architectures, including transformer and non‑transformer approaches
• Run experiments, analyze results, and refine methodologies to improve training efficiency and model quality
• Identify and resolve bottlenecks in training systems, data pipelines, and model performance
• Improve distributed training infrastructure to support next‑generation AI workloads
• Collaborate with researchers and engineers to translate experimental ideas into production‑ready training systems
• Contribute to the evolution of high‑performance AI training systems and frameworks

Requirements

• PhD or strong academic/research background in Computer Science, Machine Learning, NLP, or related fields (preferred)
• Hands‑on experience with large‑scale LLM pre‑training on distributed GPU infrastructure (thousands of GPUs)
• Strong understanding of transformer architectures and advanced model design techniques
• Experience with distributed training frameworks and large‑scale AI systems
• Proficiency in PyTorch and Hugging Face ecosystem for model development and training
• Strong skills in debugging, optimizing, and improving model and system performance
• Ability to design experiments, interpret results, and iterate on research hypotheses
• Strong collaboration and communication skills in research‑driven environments

Benefits

• Competitive compensation package aligned with AI research market standards
• Remote‑friendly and globally distributed work environment
• Opportunity to work on frontier AI research at massive scale
• Access to high‑performance computing infrastructure and large GPU clusters
• Collaborative environment with top‑tier AI researchers and engineers
• High autonomy in research direction and experimentation
• Exposure to state‑of‑the‑art AI systems and multimodal model development
• Professional growth in a fast‑evolving, innovation‑driven AI ecosystem