NLP Research Innovator

8 hours ago

Rajahmundry, Andhra Pradesh, India beBeeNaturalLanguageProcessing Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
NLP Research Scientist

Our team is seeking a skilled NLP Research Scientist with expertise in developing and implementing state-of-the-art language models.

The ideal candidate will have hands-on experience with model development, prototyping, and fine-tuning for various tasks such as clinical note summarization, entity extraction, and question answering.

Responsibilities include driving proof-of-concepts from rapid iterations to rigorous experimental evaluations on clinical-text tasks.

Candidates will collaborate closely with production teams to hand off scalable prototypes that demonstrate clear performance gains.

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 skills and deep familiarity with PyTorch or TensorFlow.
  • Proficiency in working with NLP frameworks (e.g., spaCy, NLTK, HuggingFace Transformers, LangChain, vectorDBs, etc), 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.

What You Will Be Doing:

  1. Model Innovation & Prototyping: Rapidly prototype novel LLM architectures, adapter modules, and prompt-tuning strategies for tasks such as clinical note summarization, entity extraction, and question answering.
  2. Experimental Design & Analysis: Define evaluation protocols, run ablation studies, and analyze results to quantify improvements in accuracy, robustness, and domain generalization.
  3. Proof-of-Concept Development: Build end-to-end POCs demonstrating how research breakthroughs translate into tangible gains on real healthcare datasets.
  4. Collaboration & Knowledge Sharing: Work alongside NLP engineers and MLOps teams to hand off production-ready prototypes; present findings in regular research seminars and contribute to internal or external publications.
  5. Literature & Trend Monitoring: Continuously survey the latest NLP/ML research, distill key innovations, and propose experiments to test applicability in our clinical environment.

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