LLM Research Intern

Responsibilities 

• Assess client-specific data assets and determine the appropriate adaptation strategy — continued pretraining, supervised fine-tuning, or a combination — based on the domain, data volume, and use case requirements 

• Curate, clean, structure, and prepare domain-specific datasets from raw client data for use in model training pipelines 

• Fine-tune large language models in the 70B–100B+ parameter range using techniques such as LoRA, QLoRA, and multi-adapter patterns 

• Perform continued pretraining on open-weight models (Qwen, Llama, and related ecosystems) to embed domain knowledge directly into model weights 

• Manage distributed training workflows across multi-node GPU clusters 

• Design and execute evaluation frameworks to validate domain adaptation quality, factual grounding, and model behavior 

• Support RAG system development where applicable, including vector database integration, chunking strategies, and reranking pipelines 

• Contribute to inference optimization and deployment pipeline integration 

Required Qualifications 

• Currently enrolled in or recently completed a Bachelor's, Master's, or PhD program in Computer Science, Machine Learning, or a related field 

• Demonstrated hands-on experience fine-tuning large language models, supported by concrete project work, research, or open-source contributions 

• Experience with frontier-scale models (100B+ parameters) or distributed training across multi-node GPU clusters 

• Familiarity with parameter-efficient fine-tuning methods (LoRA, QLoRA) and open-weight model architectures 

• Experience with data curation and preparation workflows for LLM training, including cleaning, formatting, deduplication, and quality filtering 

• Proficiency in Python-based ML frameworks such as PyTorch, HuggingFace Transformers, DeepSpeed, or FSDP 

• Understanding of training compute, memory constraints, and inference trade-offs at scale 

Nice to Have 

• Familiarity with RAG architectures or production inference serving frameworks (vLLM, TGI, TensorRT-LLM) 

• Experience in low-resource or multilingual NLP settings 
• Relevant publications, open-source contributions, or documented projects involving LLM training