# AI Automation & Enablement Engineer at Legora

Legora is hiring an AI Automation & Enablement Engineer in New York, United States. It pays $173k-$233k a year and Level rates it Uses AI ●●○○; you can [apply on Legora](https://jobsbylevel.com/go/9b9d2244-d99e-436e-b86d-2c313ed7c6ad?utm_medium=ai-agent).

AI Level 2, AI centrality 49 out of 100. New York City.

## Details

- Company: [Legora](https://jobsbylevel.com/companies/legora)
- AI level: AI Level 2 (score 49 out of 100)
- Location: New York City
- Salary: $173k-$233k
- Posted: October 23, 2026
- Apply: https://jobsbylevel.com/go/9b9d2244-d99e-436e-b86d-2c313ed7c6ad?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=ai-automation-enablement-engineer-at-legora-a00ef0

## Prepare for this job

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## Description

About Us Legora is redefining how legal work gets done. Not built for lawyers, built with them. We work alongside the world’s best legal teams, who expect excellence, precision, and speed, and we hold ourselves to the same bar. Our AI-native workspace lets legal professionals move faster, think more clearly, and operate with sharper precision. By analysing thousands of documents in minutes and powering end-to-end workflows, we cut through complexity, teams can focus on what matters: judgment, strategy, and outcomes. 2,100+ customers across 80+ countries trust us, including Cleary Gottlieb, Goodwin, Linklaters, White & Case, Dentons, and Barclays. We’ve scaled to $200M+ in ARR , with teams across Europe, North America and APAC, and continue to expand through acquisitions including Qura, Walter AI, Graceview, Cadastral, and Wexler. We partner with world-class performers: including Aaron Judge and the New York Yankees, Ludvig Åberg (and his caddie), and campaigns featuring Jude Law. Joining Legora means three things. We lean in: ownership over titles, outcomes over intentions. We fight for excellence: high standards, direct, ego-free feedback. We grow together: as a team and with our customers. Mission before ego. Everyone contributes. No one coasts. If you’re driven by impact, pace, and raising the bar. This is the place. About the role We want every employee at Legora to get more from AI. As an AI Automation & Enablement Engineer, your mission is to raise the baseline of AI fluency across the company and help colleagues have their own aha moments with AI and automation. You’ll work as an embedded engineering partner to internal teams: understand how people work, build useful solutions alongside them and help them develop the confidence and skills to use AI independently. You’ll combine hands-on building with practical teaching, turning promising experiments into reliable workflows that people use every day. You’ll work with tools such as Claude and Claude Code, OpenAI’s ChatGPT and Codex, Cursor, and other AI and automation platforms. You’ll choose the right approach for the problem, from a better way to use an existing tool to an integration or internal application. What you’ll be doing Find work worth improving: Spend time with employees across functions to understand their tasks and identify opportunities to improve quality, reduce manual effort or remove delays. Create practical "aha" moments: Work alongside colleagues on their real tasks. Help them provide useful context, break down problems, evaluate outputs and see where AI can meaningfully improve their work. Build automations and internal tools: Use AI coding tools, scripts, APIs, agents and workflow platforms to take useful ideas through testing, rollout and ongoing use. Raise AI fluency: Run hands-on sessions and office hours, create clear examples and reusable workflows, and help employees build skills they can apply independently. Turn individual wins into shared practice: Identify approaches that work, make them easy to reuse and help teams develop their own confident AI practitioners. Choose tools thoughtfully: Evaluate new capabilities and explain the tradeoffs in usefulness, reliability, cost and ease of adoption. Simplify the process before automating it. Optimize model selection and spend: Match models to the task, comparing quality, speed and cost to improve employee productivity and reduce unnecessary AI spend. Help teams understand when a faster, lower-cost model is sufficient and when a more capable model justifies the additional cost. Make workflows dependable: Test outputs and verify actions in connected systems. Work with IT, Security and system owners on appropriate permissions, human review, monitoring and recovery steps. Own adoption and improvement: Follow up after launch, resolve failures and improve what you build based on how people actually use it. Measure the results: Track changes in manual effort, turnaround time, errors, output quality and

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