We are looking for a talented and experienced Machine Learning Engineer with expertise in Large Language Models to join our clients team in Spain!
This role offers the opportunity to work with cutting-edge quantum and AI technologies, spearheading the design, implementation, and enhancement of our language models. You will collaborate with cross-functional teams to seamlessly integrate these models into our products, tackle complex challenges, contribute to innovative research, and help shape the future of LLM and NLP technologies.
Responsibilities:
Develop advanced techniques for compressing Large Language Models using quantum-inspired technologies to address complex problems across various domains.
Perform rigorous evaluations and benchmarks to assess model performance, identify areas for enhancement, and optimize LLMs for improved accuracy, robustness, and efficiency.
Leverage your expertise to analyze model strengths and weaknesses, propose improvements, and devise innovative solutions to boost performance and efficiency.
Serve as a subject matter expert in LLMs, addressing domain-specific challenges and exploring opportunities for quantum AI-driven innovation.
Maintain detailed documentation of LLM development processes, experiments, and findings.
Share knowledge within the team, foster continuous learning, mentor junior team members, and support their growth in LLM development.
Participate in code reviews and offer constructive feedback to peers.
Stay informed about the latest advancements and trends in LLMs, recommending relevant tools and technologies.
Qualifications:
A Master’s or Ph.D. in Artificial Intelligence, Computer Science, Data Science, or a related field.
3+ years of hands-on experience with deep learning models and neural networks, particularly in working with Large Language Models, Transformer architectures, or computer vision models.
At least 1 year of practical experience with LLMs and Transformer models, with proficiency in libraries such as HuggingFace Transformers, Accelerate, and Datasets.
Strong mathematical foundation and deep knowledge of deep learning algorithms and neural networks, covering both training and inference.
Proficient in problem-solving, debugging, performance analysis, test design, and documentation.
Solid understanding of GPU architectures and their applications.
Excellent Python programming skills and experience with libraries like PyTorch and HuggingFace.
Familiarity with cloud platforms (preferably AWS), containerization tools like Docker, and deploying AI solutions in cloud environments.
Strong written and verbal communication skills with the ability to work collaboratively in a dynamic team environment and convey complex ideas effectively.
Research publications in deep learning are a plus.
Fluent in English and Spanish is a must.
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