AI Research Engineer

- Deep understanding of the latest developments in NLP and generative AI. 
- Proficiency in PyTorch, including an in-depth knowledge of its internal mechanisms and optimization techniques. 
- Expertise in handling complex neural network architectures, particularly those involving multi-layer transformers and custom activation functions like SwiGLU. 
- Familiarity with advanced techniques in machine learning, such as fine-tuning, regularization, and model interpretability. 

Qualifications: 

Education: 
- PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field, with a focus on NLP and generative AI. 

Experience: 
- Extensive hands-on experience with transformer architectures and their applications in NLP. 
- Demonstrated experience in building and deploying large-scale generative AI models, especially using PyTorch. 
- Strong background in theoretical and applied aspects of AI, with a portfolio of projects demonstrating practical successes and innovations. 

Technical Skills: 
- Deep understanding of the latest developments in NLP and generative AI. 
- Proficiency in PyTorch, including an in-depth knowledge of its internal mechanisms and optimization techniques. 
- Expertise in handling complex neural network architectures, particularly those involving multi-layer transformers and custom activation functions like SwiGLU. 
- Familiarity with advanced techniques in machine learning, such as fine-tuning, regularization, and model interpretability. 

Preferred Qualifications: 
- Published research or active contributions to open-source projects in the field of AI, particularly related to transformers and NLP. 
- Experience with cloud-based AI model deployment and management on platforms like AWS, Google Cloud, or Azure