Staff/Senior Staff Software Engineer (Agentic Search)

• Ironclad’s Intelligence Platform team owns Agent Assistant, Conversational Search, and Content Understanding — the systems that help customers and AI agents understand, find, and act on the right contract information. These are the flagship AI capabilities of our product, built and operated by a combined team of ML and ML infrastructure engineers
• We have multiple roles open, and are hiring a range of levels — Staff and Senior Staff. As a Staff or Senior Staff Engineer, Agentic Search, you’ll own the architecture that combines LLMs and retrieval systems to answer complex, ambiguous questions about a customer’s contracts, and you’ll set the technical direction that other engineers across the AI organization build on
• You’ll partner closely with product, applied science, and engineering leaders to raise the company’s search quality bar, and you’ll bring the technical depth and eval-driven rigor to turn ambiguous problems into shipped, measurable improvements. Scope and ownership will be calibrated to level
• Own agentic search architecture. Design and evolve the systems that combine LLMs and retrieval to produce optimal answers to complex or ambiguous questions
• Drive eval-driven development. Design and run the benchmarks and experiments that measure search quality, and use that feedback to continuously improve the system
• Raise the search quality bar. Contribute to and influence the company’s overall search quality standard
• Own content understanding and ingestion. Turn raw documents into processed data that retrieval systems can consume, by building and using NLP/LLM models and pipelines
• Set technical direction. Define architectural decisions and technical direction that other engineers across the AI organization build on

Benefits

• Dental, and vision insurance
• 401K
• Wellness reimbursement
• Flexible vacation policy
• Generous parental leave for both primary and secondary caregivers
• Work from home opportunities
• Health insurance

• Fluency with modern LLM APIs and multi-provider orchestration (Anthropic, OpenAI, Google) — reasoning about token budgets, provider-specific tool-calling semantics, and prompt-caching trade-offs
• Demonstrated depth in one of: learned/hybrid retrieval (lexical + vector + reranking), query understanding/NLU pipelines, or production LLM agent systems — ideally more than one
• 10+ years building production systems, with a substantial portion in search, information retrieval, content understanding, or recommendation systems at meaningful scale
• Experience with search frameworks (Elasticsearch or equivalent — Solr, Vespa, OpenSearch; embedding search) in production, including relevance tuning and reranking
• Comfortable operating in a dynamic, fast-paced, outcome-driven environment
• Strong autonomy, ownership, and technical leadership across teams, including mentoring senior engineers and driving architectural decisions
• Experience building eval-driven workflows — offline benchmarks, regression detection, structured A/B comparison — as opposed to shipping and hoping
• Hands-on experience with post-training algorithms and infrastructure, including SFT and RL
• Experience with content understanding and/or information retrieval in structured-document-heavy domains
• Prior work on RAG systems involving data sources in different formats (Google Docs, PDFs, DOCX, etc.)