Key Responsibilities
- Design and implement state-of-the-art large language models (LLMs) for domain-specific applications
- Optimize model architectures, training strategies, and inference pipelines for efficiency and accuracy
- Collaborate with cross-functional teams to integrate LLMs into production systems
- Develop novel techniques for fine-tuning, alignment, and safety in language models
- Conduct rigorous experiments to evaluate model performance and generalization
- Publish research findings and contribute to open-source initiatives
Requirements
- PhD in Computer Science, ML, or a related field with a focus on NLP/LLMs
- Proven track record in developing and deploying LLMs (e.g., fine-tuning, RLHF, or prompt engineering)
- Strong programming skills in Python and familiarity with ML frameworks (PyTorch/JAX)
- Experience with distributed training and GPU-accelerated computing
- Publications in top-tier conferences (NeurIPS, ICML, ACL) are highly desirable