About Mage Legal

Mage Legal is an AI-native legal technology company built for M&A, PE, and corporate legal teams. Our platform automates core diligence workflows—issue spotting, disclosure schedules, and first-pass diligence outputs—used in live, high-stakes transactions. We’re scaling adoption inside law firms and funds, testing distribution channels, refining our positioning, and building the legal AI experience attorneys actually trust under deal pressure.

Who We’re Building For (ICP)

Deal attorneys, PE professionals, and in-house legal teams working on fast-moving M&A and growth transactions. Typically Big Law, elite performers, highly analytical, and extremely risk-sensitive. They care about accuracy, auditability, and credibility more than novelty. They’re skeptical of “AI for law,” pressed for time, and allergic to tools that create cleanup work. Your work will shape how this audience discovers Mage Legal, evaluates it, and ultimately trusts it enough to use on real deals.

What You’ll Do

At Mage Legal, every team member can propose an experiment. You’ll help shape hypotheses, contribute your own ideas, and turn them into live tests across acquisition, activation, and workflow adoption—always grounded in real attorney behavior.
Hypothesis → Launch → Measure → Iterate.

• Design and run growth experiments across channels relevant to legal buyers (founder-led sales, referrals, content, partnerships, targeted outbound, events, and communities).
• Test and refine positioning, messaging, and offers that resonate with law firms, PE funds, and in-house teams (e.g., pilot structure, deal-based pricing, PoCs, workflow entry points).
• Collaborate with engineering to spin up lightweight demos, onboarding flows, and workflow experiments for rapid testing inside real diligence scenarios.
• Translate insights into product and GTM improvements, working closely with product and design to bake winning workflows into the core platform.

Must-Haves

• Background in B2B product experimentation, developer tools, legal tech, fintech, or other high-trust enterprise software.
• Proven ability to design, launch, and analyze experiments across non-consumer channels with long sales cycles and high credibility requirements.
• Strong grasp of analytics, funnel design, and qualitative signal; able to combine data with real customer conversations.
• Deep curiosity about professional workflows, incentives, risk tolerance, and what drives adoption in conservative, high-stakes environments.
• Excellent communication skills; able to translate messy signals into clear recommendations for product, sales, and leadership.
• You take the work seriously and aim for excellence, but bring energy, creativity, and zero pretense—we move fast, test often, and care deeply about quality.

Nice-to-Haves

• Experience selling to or working with law firms, PE, or enterprise professional services.
• Familiarity with M&A workflows, diligence processes, or legal operations.
• Experience shaping early-stage positioning, pricing, or pilot programs.
• Previous startup or early-stage experience wearing multiple hats.

Why You’ll Love Working Here

• High-velocity culture: Ideas ship quickly; experiments run in days, not quarters.
• Real stakes: Help shape AI systems used in live deals where accuracy and trust matter.
• Clear product-market signal: Strong pull from sophisticated legal buyers who feel today’s tools don’t work.
• Tight founder collaboration: Work directly with the founder building and deploying the product.
• AI-first from day one: No legacy constraints, no “AI wrapper” thinking—this is infrastructure, not add-ons.

About the Interview

• Screening call with the CEO, Raffi
• Take-home assessment
• On-site interview

This role is based in person in San Francisco, CA (not remote).

Responsibilities

• Backend AI, typescript role
• Collaborate with the CEO/CTO to design, develop, and implement key features of our AI realtor platform.
• Design AI prompts for messaging and deal automation flows (texts and emails).
• Stay up-to-date with industry trends and emerging technologies.

Qualifications

• Uses AI to ship code fast
• Has previously shipped consumer-facing features

Nice to haves

• Public github contributions
• Previous experience