
NLP Research Position
7 days ago
Role Overview:
NLP ResearcherThis role focuses on driving innovative research and development in the field of natural language processing (NLP), with a specific emphasis on exploring and implementing state-of-the-art large language model (LLM) architectures, prompt engineering strategies, and LLM training techniques tailored to healthcare and oncology text.
The ideal candidate will possess strong Python skills, deep familiarity with PyTorch or TensorFlow, and expertise in NLP frameworks, deep learning libraries, and common data preprocessing techniques.
Key Responsibilities:
- Design and implement novel LLM architectures, adapter modules, and prompt-tuning strategies for tasks such as clinical note summarization, entity extraction, and question answering.
- Develop and evaluate evaluation protocols, run ablation studies, and analyze results to quantify improvements in accuracy, robustness, and domain generalization.
- Build end-to-end proof-of-concepts demonstrating how research breakthroughs translate into tangible gains on real healthcare datasets.
- Collaborate with NLP engineers and MLOps teams to hand off production-ready prototypes; present findings in regular research seminars and contribute to internal or external publications.
- Continuously survey the latest NLP/ML research, distill key innovations, and propose experiments to test applicability in our clinical environment.
Requirements:
- Bachelor's or Master's degree in Computer Science, Computational Linguistics, AI/ML, or a related field
- 2–3 years of professional R&D experience in NLP or ML research roles
- Strong Python programming skills and deep familiarity with PyTorch or TensorFlow
- Proficiency in working with NLP frameworks (e.g., spaCy, NLTK, HuggingFace Transformers, LangChain, vectorDBs), deep learning libraries (e.g., PyTorch), and common data preprocessing techniques.
- Expertise in prompt engineering, few-shot learning, and fine-tuning for domain-specific tasks
- Familiarity with building retrieval-augmented generation (RAG) pipelines.
- Hands-on experience with LLM fine-tuning using LoRA and SFT methods
- Solid understanding of experimental design, ablation studies, and statistical significance testing
- Familiarity with clinical or biomedical NLP is a plus
- If you have worked with Direct Preference Optimization (DPO), that's a plus
- Excellent communication skills for sharing insights in seminars, whitepapers, or publications
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