About the RoleWoodfrog 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.
Key Responsibilities- 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.
Required Skills & Qualifications- 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.
Preferred Qualifications- 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.
