About the Role
We’re hiring an AI Engineer to design, build, and deploy production-grade LLM systems that power our legal platform. This role is focused on applied AI, turning real-world legal workflows into reliable, agent-driven systems that operate over documents, messages, and structured data.
You’ll work at the core of our AI layer, building agents, evaluation systems, and data pipelines that directly impact how legal work is performed. The environment is highly iterative and performance-driven, with a strong emphasis on reliability, correctness, and real-world usability, not just demos.
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What You’ll Do
Design and deploy LLM-powered systems for document understanding, drafting, and workflow automation
Build and orchestrate agent-based systems (e.g., LangChain / Deep Agents) for complex, multi-step tasks
Develop and maintain evaluation frameworks to measure accuracy, reliability, and regressions
Implement data pipelines for ingesting and structuring documents (including OCR workflows)
Improve system performance through prompt design, retrieval strategies, and tool integration
Work closely with product and engineering to translate legal workflows into AI-native systems
Debug and optimize long-running agent jobs and failure modes in production
Contribute to overall AI architecture, including memory, context management, and retrieval layers
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Qualifications
3–7 years of engineering experience, or a strong track record building and shipping AI systems (excluding internships)
Proficiency in TypeScript and experience building production backend systems
Hands-on experience with LLM frameworks (e.g., LangChain) and agent architectures
Experience designing and running evals (benchmarks, test sets, regression tracking)
Familiarity with document processing pipelines, including OCR and unstructured data extraction
Strong understanding of prompt engineering, retrieval (RAG), and tool-augmented generation
Ability to operate in ambiguous, fast-moving environments and own systems end-to-end
Startup or high-velocity experience preferred