NLP Engineer

Key Responsibilities

• Design, train, fine-tune, and deploy NLP models for tasks such as classification, extraction, summarization, and search
• Build and maintain data pipelines for text preprocessing, annotation, and feature engineering
• Evaluate and integrate large language models (LLMs) through fine-tuning, prompt engineering, and RAG architectures
• Develop evaluation frameworks including benchmarks, metrics, and human evaluation protocols
• Collaborate with product and engineering teams to define NLP-powered features and ship them to production
• Stay current with NLP research and assess new techniques for practical applicability
• Optimize models for latency, cost, and accuracy in production environments

Required Skills & Experience

• 3+ years of experience building NLP systems in a production environment
• Strong proficiency in Python and NLP/ML libraries (Hugging Face Transformers, spaCy, NLTK)
• Experience with transformer architectures and modern language models (BERT, GPT, T5, LLaMA)
• Hands-on experience with fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)
• Solid understanding of NLP fundamentals: tokenization, embeddings, attention mechanisms, sequence modeling
• Experience with ML experiment tracking and model versioning (MLflow, Weights & Biases)
• Strong software engineering practices: version control, testing, code review, CI/CD

Nice-to-Have

• Experience with vector databases (Pinecone, Weaviate, Milvus) for semantic search
• Familiarity with LLM deployment and serving (vLLM, TensorRT-LLM, Triton)
• Publications or contributions to the NLP research community
• Experience with multilingual NLP or low-resource language challenges
• Background in speech-to-text or multimodal AI systems

Tech Stack

What We Offer

• Competitive salary and equity package
• Flexible remote or hybrid work arrangement
• Health, dental, and vision insurance
• Annual learning and development budget
• Generous PTO policy

Interview Process

1. 1Recruiter phone screen (30 min)
2. 2Technical phone screen with an ML engineer covering NLP fundamentals and system design (45 min)
3. 3Take-home or live coding exercise: NLP task implementation (3-4 hours estimated)
4. 4On-site or virtual deep dive: model design, evaluation methodology, and past project walkthrough (3 hours)
5. 5Final conversation with the hiring manager or ML lead