NLP Research ScientistJob 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.
