Senior Machine Learning Engineer (LLM & GPU Architecture)
Role details
Job location
Tech stack
Job description
In this role you will have the opportunity to leverage cutting-edge quantum and AI technologies to lead the design, implementation, and improvement of our language models, as well as working closely with cross-functional teams to integrate these models into our products. You will have the opportunity to work on challenging projects, contribute to cutting-edge research, and shape the future of LLM and NLP technologies., * 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.
- Use your expertise to assess the strengths and weaknesses of models, propose enhancements, and develop novel solutions to improve performance and efficiency.
- Act as a domain expert in the field of 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.
Requirements
- 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.
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