Founding AI Engineer

About Vector Legal

Vector Legal is a modern solution for startup legal: combining a full-service law firm with a purpose-built AI (VectorAI) platform. We're backed by some of the best investors in Silicon Valley, including Y Combinator, Base10, Transpose Platform, and others, to reinvent the way that legal solutions are delivered for Startups.

We support companies across their lifecycle, including complex formations, fundraising and priced rounds, commercial contracting, hiring and team changes, mergers and acquisitions, and dissolutions. VectorAI powers agentic data rooms, proprietary draft/review/negotiate tools, e-signatures, cap table management, legal health checks and more.

Our goal is to make high-quality legal work faster, more scalable, and more cost-efficient.

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.

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

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

Originally posted on Himalayas