Research Scientist

1 week ago

Pushkar, Rajasthan, India beBeeNlp Full time ₹ 25,00,000 - ₹ 40,00,000

We are seeking a driven NLP Research Engineer with 2–3 years of hands-on experience in model development and prototyping.

As an R&D-focused team member, you will explore and implement state-of-the-art LLM architectures, prompt engineering strategies, and LLM training techniques tailored to healthcare and oncology text.

Your primary responsibility is to drive proof-of-concepts—from rapid iterations to rigorous experimental evaluations on clinical-text tasks.

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
  • Excellent communication skills for sharing insights in seminars, whitepapers, or publications

Responsibilities:

  • 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.
  • Experimental Design & Analysis: Define evaluation protocols, run ablation studies, and analyze results to quantify improvements in accuracy, robustness, and domain generalization.
  • Proof-of-Concept Development: Build end-to-end POCs demonstrating how research breakthroughs translate into tangible gains on real healthcare datasets.
  • 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.
  • 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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