Software Engineer - NLP/LLM Reasoning

Woodfrog is hiring Software Engineers – NLP/LLM Reasoning for one of our deep-tech clients building advanced reasoning models for enterprise applications using Large Language Models (LLMs). This is an opportunity to work at the intersection of cutting-edge AI research and production engineering, developing intelligent systems that solve complex real-world business problems.

As a Software Engineer, you will be responsible for designing, developing, and optimizing enterprise-grade reasoning models while researching innovative NLP techniques and integrating them into scalable production systems. You will collaborate with cross-functional teams to deliver high-quality AI solutions, maintain clean and efficient code, and continuously improve model performance.

This role is best suited for engineers who enjoy solving challenging problems, thrive in fast-paced startup environments, and are passionate about advancing the capabilities of LLMs and reasoning systems.

• Design, develop, and optimize foundational reasoning models for enterprise applications.
• Research and implement advanced NLP techniques and reasoning paradigms.
• Build, maintain, and improve production-ready AI systems powered by Large Language Models.
• Write clean, efficient, scalable, and well-documented Python code.
• Collaborate with product, engineering, and research teams to define technical solutions.
• Monitor, troubleshoot, and improve model performance and system reliability.
• Stay up to date with the latest developments in NLP, LLMs, and AI research, and apply them to real-world products.

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Linguistics, or a related field.
• Strong programming skills in Python.
• Hands-on experience with Natural Language Processing (NLP) and Large Language Models (LLMs).
• Experience with TensorFlow, PyTorch, or similar deep learning frameworks.
• Good understanding of transformer architectures, prompt engineering, embeddings, and reasoning techniques.
• Strong analytical, debugging, and problem-solving skills.
• Excellent communication and collaboration abilities.
• Ability to work independently in a high-ownership, fast-paced environment.

• Experience with Retrieval-Augmented Generation (RAG), Vector Databases, or Agentic AI.
• Familiarity with LLM fine-tuning, evaluation, and optimization.
• Experience deploying AI models in production environments.
• Knowledge of cloud platforms such as AWS, Azure, or GCP.