NLP Research Scientist

NLP Research Scientist

Job Title

NLP Research Scientist

Job Summary

We are seeking an innovative NLP Research Scientist to develop cutting-edge Natural Language Processing (NLP) and Large Language Model (LLM) solutions for real-world applications. The ideal candidate will have a strong background in machine learning, deep learning, and modern NLP techniques, with experience conducting research, developing state-of-the-art models, and translating research into scalable production systems. You will collaborate with multidisciplinary teams to build intelligent language applications that drive business impact.

Key Responsibilities

• Conduct research in Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI.

• Design, develop, and evaluate NLP models for tasks such as text classification, named entity recognition (NER), question answering, summarization, sentiment analysis, machine translation, and conversational AI.

• Develop and fine-tune transformer-based models including BERT, RoBERTa, T5, GPT, Llama, Mistral, Gemma, and other foundation models.

• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.

• Design scalable data preprocessing, model training, evaluation, and inference pipelines.

• Perform prompt engineering, supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), LoRA, and QLoRA for domain-specific applications.

• Conduct experiments, benchmark models, analyze results, and optimize model performance for accuracy, latency, and cost.

• Collaborate with ML engineers, data scientists, software engineers, and product teams to deploy NLP solutions into production.

• Stay up to date with the latest advancements in NLP, LLMs, retrieval systems, and generative AI through research papers, conferences, and open-source communities.

Required Qualifications

• Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, Data Science, or a related field.

• 3+ years of experience in NLP research or applied machine learning.

• Strong understanding of:

  • Natural Language Processing

  • Deep Learning

  • Transformer Architectures

  • Attention Mechanisms

  • Representation Learning

  • Language Modeling

  • Information Retrieval

  • Prompt Engineering

• Proficiency in Python.

• Hands-on experience with PyTorch or TensorFlow.

• Experience with Hugging Face Transformers, Sentence Transformers, spaCy, NLTK, or similar NLP frameworks.

• Strong understanding of NLP evaluation metrics such as BLEU, ROUGE, METEOR, Precision, Recall, F1-Score, and BERTScore.

• Experience with Git, Docker, Linux, and cloud platforms (AWS, Azure, or Google Cloud).

Preferred Qualifications

• Experience with Retrieval-Augmented Generation (RAG) architectures.

• Experience with vector databases such as FAISS, Pinecone, Milvus, Weaviate, or Chroma.

• Hands-on experience with LLM fine-tuning techniques including LoRA, QLoRA, and PEFT.

• Familiarity with distributed training frameworks such as DeepSpeed or PyTorch Distributed.

• Experience with reinforcement learning from human feedback (RLHF) or preference optimization techniques.

• Publications in leading AI or NLP conferences such as ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, or COLING.

• Contributions to open-source NLP or AI projects.

Technical Skills

• Python

• PyTorch / TensorFlow

• Hugging Face Transformers

• Sentence Transformers

• spaCy

• NLTK

• LangChain

• LlamaIndex

• FAISS

• Pinecone

• Milvus

• Docker

• Kubernetes

• MLflow

• Git

• Linux

• SQL

• AWS / Azure / Google Cloud

Soft Skills

• Strong analytical and research mindset.

• Excellent problem-solving and critical thinking skills.

• Effective communication and technical writing abilities.

• Ability to collaborate with cross-functional teams.

• Curiosity and passion for advancing NLP and AI research.

Nice to Have

• Experience with multimodal AI and vision-language models.

• Knowledge of AI agents and agentic workflows.

• Experience with synthetic data generation and evaluation.

• Familiarity with MLOps, model serving, and CI/CD pipelines.

• Experience with knowledge graphs and semantic search.

Benefits

• Competitive salary and performance-based incentives.

• Flexible work arrangements.

• Comprehensive health and wellness benefits.

• Learning, certification, and conference sponsorship opportunities.

• Access to high-performance GPU infrastructure.

• Opportunity to work on cutting-edge NLP and Generative AI research in a collaborative environment.