Research Scientist

14 hours ago

Tiruppur, Tamil Nadu, India beBeeNLP Full time ₹ 18,00,000 - ₹ 30,00,000
NLP Research Engineer

We are seeking a driven individual with 2–3 years of hands-on experience in model development and prototyping to join our team.

This role will involve exploring and implementing state-of-the-art LLM architectures, prompt engineering strategies, and LLM training techniques tailored to healthcare and oncology text. The primary responsibility will be to drive proof-of-concepts—from rapid iterations to rigorous experimental evaluations on clinical-text tasks.

You will work closely with our production teams to hand off scalable prototypes that demonstrate clear performance gains.

Key qualifications include:

  • 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

As an NLP Research Engineer, you will be responsible for the following tasks:

  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.

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