Chief Language Model Architect

beBeeLlm; Madrid; Presencial; EUR 70.000 - 100.000; Jornada completa

Descripción de la vacante

An AI technology firm in Madrid seeks a Lead Deep Learning Innovator to spearhead the design and enhancement of advanced Large Language Models. The ideal candidate will bring over three years of experience in deep learning, particularly with Transformer architectures, and possess strong programming skills in Python. You'll work collaboratively to drive innovation in NLP technologies and contribute significantly to research projects. This role promises to be intellectually stimulating and influential in shaping the industry.

Servicios

Cutting-edge technology exposure

Cross-functional collaboration opportunities

Opportunity to innovate in NLP

Formación

• 3+ years of hands-on experience with deep learning models.
• Solid mathematical foundations and expertise in algorithms.
• Experience deploying AI solutions in cloud environments.

Responsabilidades

• Designing and developing techniques for Large Language Models.
• Conducting evaluations and benchmarks of model performance.
• Documenting LLM development processes and results.

Conocimientos

Deep learning models

Neural networks

Python programming

Problem-solving

Collaboration in teams

Educación

Master's or Ph.D. in relevant fields

Herramientas

HuggingFace Transformers

PyTorch

Docker

AWS

Descripción del empleo

Lead Deep Learning Innovator

This is a great opportunity to leverage cutting-edge technologies and lead the design, implementation, and improvement of large language models.

Job Overview

In this role, you will have the opportunity to work on challenging projects, contribute to cutting-edge research, and shape the future of NLP technologies. You will be responsible for :

• Designing and developing new techniques to compress Large Language Models based on quantum-inspired technologies to solve challenging use cases in various domains.


• Conducting rigorous evaluations and benchmarks of model performance, identifying areas for improvement, and fine-tuning and optimising LLMs for enhanced accuracy, robustness, and efficiency.


• Using expertise to assess the strengths and weaknesses of models, proposing enhancements, and developing novel solutions to improve performance and efficiency.


• Maintaining comprehensive documentation of LLM development processes, experiments, and results.


• Participating in code reviews and providing constructive feedback to team members.

Required Skills and Qualifications

Master\'s or Ph.D. in Artificial Intelligence, Computer Science, Data Science, or related fields.

3+ years of hands-on experience with deep learning models and neural networks, preferably working with Large Language Models and Transformer architectures, or computer vision models.

Hands-on experience using LLM and Transformer models, with excellent command of libraries such as HuggingFace Transformers, Accelerate, Datasets, etc.

Solid mathematical foundations and expertise in deep learning algorithms and neural networks, both training and inference.

Excellent problem-solving, debugging, performance analysis, test design, and documentation skills.

Strong understanding with the fundamentals of GPU architectures.

Excellent programming skills in Python and experience with relevant libraries (PyTorch, HuggingFace, etc.).

Experience with cloud platforms (ideally AWS), containerization technologies (Docker) and with deploying AI solutions in a cloud environment.

Excellent written and verbal communication skills, with the ability to work collaboratively in a fast-paced team environment and communicate complex ideas effectively.

Previous research publications in deep learning is a plus.

Benefits

Work on cutting-edge technology and contribute to shaping the future of NLP.

Collaborate with cross-functional teams to integrate language models into products.

Opportunity to develop novel solutions and enhance performance and efficiency.

Chance to participate in code reviews and provide constructive feedback.

Others

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Key Words : Large Language Models / LLM / Machine Learning / AI / Quantum Computing / GPU Architecture / GPGPU / GPU Farms / Multi-GPU / AWS / Kubernetes Clusters / DeepSpeed / SLURM / RAY / Transformer Models / Fine-tuning / Mistral / Llama