Staff Software Engineer, Ironclad

• 2 years of previous experience exploring and applying cutting-edge techniques in NLP, LLM integration, Prompt / Context Engineering, and semantic search to advance Ironclad’s AI/ML vision
• 2 years of previous experience designing and maintaining robust systems for context management, model training / tuning, orchestration, evaluation & inference, with a focus on scalability, reliability, and observability
• 2 years of previous experience developing and scaling retrieval-augmented generation (RAG) systems and agent-based orchestration frameworks for complex legal workflows
• BS or advanced degree in Computer Science, Engineering, or a related field
• Experience working with large language models (e.g., GPT, Claude) or building RAG systems
• Experience with embedding models and vector databases
• Experience in training, evaluating, and serving AI/ML models
• 7+ years of hands-on experience with Python, backend service development, and RESTful APIs
• Experience in NLP techniques such as text classification, entity extraction, document understanding, or question answering
• Proficiency with AI frameworks and APIs (e.g., PyTorch, TensorFlow, HuggingFace , OpenAI , LangChain)
• Experience with cloud platforms (e.g., GCP, AWS, Azure)
• Solid understanding of Git or other version control systems
• Strong engineering fundamentals and attention to code quality, testing, and maintainability
• Excellent communication and collaboration skills
• Passion for building AI products that solve real-world customer problemsS

Desirable

• We're looking for a AI/ML Engineer to help shape the future of legal AI
• In this role, you’ll work with cutting-edge technologies such as Context Engineering, LLM Tuning / Post Training, Evaluation Frameworks to develop state-of-the-art models and intelligent systems that power product features used by leading legal teams
• You’ll partner closely with product managers and designers to translate user needs into AI-powered product capabilities
• You’ll also lead efforts in model evaluation, iteration, and deployment—ensuring our systems are robust, explainable, and continuously improving
• As part of our end-to-end ownership model, you’ll contribute to the MLOps stack to ensure our models are scalable, reliable, and performant in production
• Build and Tune models and AI capabilities that ingest legal documents, extract key information, and deliver actionable insights to customers
• Implementing backend services that power document understanding, classification, and natural language search
• Help build foundational AI/ML infrastructure, including tools for experimentation, model versioning, and online/offline performance monitoring
• Partner closely with product managers and designers to shape user-facing AI experiences and deliver value through APIs and intelligent features