Legal Engineer, Innovation & AI

Legal Engineer, Innovation & AI

London, UK

Are you ready to play a central role in reshaping how our lawyers use AI to deliver complex cross-border legal work, as part of our award-winning Innovation & AI function and Applied AI Lab? Legal Engineers sit at the heart of our Innovation & AI agenda. They design and build AI-powered workflows that let our lawyers work faster, more consistently and at higher quality, translating complex legal processes into structured, AI-executable systems. This is a multidisciplinary role that pulls from legal practice, AI engineering and product thinking, and is central to how we embed AI into everyday legal work. The role forms part of the Firm’s Applied AI Lab, a lean, high-performance unit built to experiment, prototype and ship AI-powered solutions that change how elite legal work gets done. Legal Engineers work closely with senior practitioners, Innovation & AI leadership and cross-functional teams to turn tacit legal knowledge into robust, data-driven workflows and tools. We are keen to meet people who are excited by generative AI and large language models, who enjoy structuring messy processes into repeatable systems, and who are comfortable working shoulder-to-shoulder with lawyers, technologists and external partners in a fast-paced, experimental environment.

Key Responsibilities

• AI Workflow Design and Execution

• Design and build AI-powered workflows that help our lawyers work faster, more consistently and at higher quality, decomposing complex legal processes into discrete, measurable tasks that AI can execute reliably.

• Re-engineer existing manual legal workflows for AI-first execution, determining where AI adds the most value, where human judgment remains essential and how to design effective handoffs between them.

• Translate the tacit knowledge of senior practitioners, including playbooks, negotiation positions, house style and drafting conventions, into structured artefacts such as prompts, schemas, decision trees and evaluation rubrics that production AI systems can execute against.

• Standards, Quality and Evaluation

• Own prompt engineering standards, schema specifications and quality assurance protocols, bui