Senior Machine Learning Engineer (LLM & GPU Architecture)
Overview
This is a great opportunity to work with one of the biggest growing tech start-ups based in Spain. They are well-funded and are a leading quantum software company in Europe, delivering practical AI and quantum-enabled applications to industries such as finance, energy, manufacturing, defence, cybersecurity, life sciences, and chemistry.
Job Overview
In this role you will leverage cutting-edge quantum and AI technologies to lead the design, implementation, and improvement of our language models, and work closely with cross-functional teams to integrate these models into our products. You will work on challenging projects, contribute to cutting-edge research, and shape the future of LLM and NLP technologies.
Responsibilities
* Design and develop new techniques to compress Large Language Models based on quantum-inspired technologies to solve challenging use cases in various domains.
* Conduct rigorous evaluations and benchmarks of model performance, identifying areas for improvement, and fine-tuning and optimising LLMs for enhanced accuracy, robustness, and efficiency.
* Assess strengths and weaknesses of models, propose enhancements, and develop novel solutions to improve performance and efficiency.
* Act as a domain expert in LLMs, understanding domain-specific problems and identifying opportunities for quantum AI-driven innovation.
* Maintain comprehensive documentation of LLM development processes, experiments, and results.
* Participate in code reviews and provide constructive feedback to team members.
Required 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 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, including training and inference.
* Excellent problem-solving, debugging, performance analysis, test design, and documentation skills.
* Strong understanding 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 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.
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
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