Hire NLP developer who can design and deploy advanced language models that power next-generation AI applications. This job description template will help you create a structured and compelling listing that attracts candidates with deep expertise in natural language processing, machine learning, and computational linguistics.
About the company
Use this section to introduce your company’s mission and the collaborative, research-oriented environment you offer for NLP specialists.
At [Company Name], we focus on developing intelligent language technologies that drive human-computer interaction forward. Our team blends academic research with real-world product innovation, solving complex NLP problems in multilingual, multimodal environments. We offer NLP engineers an opportunity to work on production-scale applications while contributing to advancements in language understanding and generation across industries.
Provide a high-level summary of what the role involves, outlining core responsibilities and areas of impact.
We are hiring NLP developer who can build scalable, high-precision language models for real-world use cases. In this role, you will develop NLP pipelines, implement transformer-based architectures, and collaborate across AI, product, and data teams to deliver cutting-edge applications. Your work will support intelligent systems such as virtual assistants, sentiment analysis engines, and document classification platforms deployed at scale.
List key job responsibilities in a clear, actionable format. These should reflect the candidate’s role in driving research, model development, and deployment.
- Design and implement NLP algorithms using transformer models (e.g., BERT, GPT) and custom deep learning architectures
- Develop preprocessing workflows including tokenization, named entity recognition, POS tagging, and dependency parsing
- Fine-tune domain-specific language models using frameworks such as Hugging Face Transformers, TensorFlow, or PyTorch
- Build production-grade NLP applications including chatbots, virtual assistants, summarization tools, and classification systems
- Develop multilingual NLP solutions for translation, language detection, and cross-lingual search
- Collaborate with engineering teams to integrate models into scalable data pipelines and cloud infrastructure
- Optimize NLP systems for accuracy, latency, and scalability, leveraging evaluation metrics and iterative improvements
- Conduct research experiments, track emerging NLP trends, and contribute to ongoing innovation efforts within the AI team
List essential qualifications, programming proficiencies, and foundational knowledge expected from qualified candidates.
- Master’s degree in Computer Science, Artificial Intelligence, Computational Linguistics, or related field
- 3–5 years of hands-on experience in building NLP systems using modern language models
- Strong command of Python and experience with NLP libraries such as NLTK, spaCy, Gensim, and Hugging Face Transformers
- Deep understanding of machine learning concepts and experience with TensorFlow, PyTorch, or Keras
- Familiarity with word embeddings (Word2Vec, GloVe, FastText) and modern transformer-based model training
- Knowledge of linguistic principles (syntax, semantics, morphology) for linguistically informed model design
- Experience deploying NLP models using cloud services (AWS, GCP, Azure) and managing resource scalability
- Strong mathematical background in statistics, probability, and linear algebra for ML algorithm development
This optional section adds additional criteria that can help identify more advanced or research-focused candidates.
- Experience with ASR, TTS, or voice user interface development
- Familiarity with information retrieval, recommendation systems, and semantic search
- Understanding of knowledge graphs, GNNs, and semantic web technologies
- Exposure to MLOps workflows, automated deployment pipelines, and model lifecycle management
- Experience with distributed systems, big data tools (e.g., Spark, Hadoop), and scalable training environments
- Knowledge of multiple programming languages (e.g., R, Scala, Java) used in NLP ecosystems
- Contributions to peer-reviewed publications, open-source NLP projects, or novel algorithm development
- Proficiency in model evaluation techniques, A/B testing, and performance optimization for NLP systems
Highlight what your organization offers to support technical growth, well-being, and professional advancement.
- Competitive compensation with performance bonuses and equity participation
- Comprehensive health, dental, and vision insurance with employer contributions
- Remote and hybrid work flexibility with core collaboration hours
- Annual budget for conferences, research papers, online training, and advanced NLP courses
- Access to GPU clusters, large datasets, cloud-based model training environments, and proprietary language tools
- Research-focused environment with weekly paper readings, knowledge-sharing sessions, and mentorship programs
- Generous paid time off, mental wellness resources, and sabbatical opportunities for long-term innovation work
- Publication support, patent incentives, and speaking opportunities at major NLP and AI conferences
This NLP developer job description template is structured to help you attract candidates who excel in building intelligent, language-powered systems. Customize each section to reflect your product goals, infrastructure, and research culture. Hire NLP developer who can lead innovation in natural language understanding and help shape the future of AI-enabled communication.
