Principal, Legal AI Engineering

Match Group runs some of the most-used apps in the world, including Tinder, Hinge, Match, OkCupid, and Plenty of Fish, for tens of millions of people in 170+ countries. That makes our legal work unusually interesting: privacy for some of the most personal data people share, trust and safety at global scale, AI features shipping into consumer products, and regulation that keeps moving.

This role sits at the intersection of legal practice and applied AI, closer to an in-house R&D function than a traditional legal team role. It's for a tech-forward builder, an engineer or product person, who can find where AI can do legal work that humans do today (or work humans can't do at scale), prototype and ship tools and agents to handle it, and iterate with lawyers in the loop until those tools are part of how the function runs.

This is about applying AI to the substance of legal work: the research, analysis, judgment calls, contract review, and regulatory tracking that lawyers do every day. It is not about optimizing existing processes or rolling out off-the-shelf legal tech. You'll build things that didn't exist before, prototype quickly, evaluate rigorously, and ship what works.

You'll serve brands across Match Group's portfolio (Tinder, Hinge, Match, OkCupid, Plenty of Fish, and others) and work with Legal leadership and attorneys as your domain experts and end users. You'll partner with engineering, product, and other technical teams where deeper infrastructure is needed. You'll have a mandate to pick where to start, prove value, and scale what works across brands.

Why This Role Matters

AI is changing what's possible in legal practice on a timeline measured in months, not years. This role is our bet that the right way to capture that is to put a dedicated builder inside Legal and have them ship.

For the right person, this is an open mandate: real problems across a portfolio of brands, lawyers and leadership who want to lean into AI, and the freedom to define what an AI-native legal function looks like at Match Group.

You'll build for one sophisticated in-house team, not for customers, so what you ship gets used, measured, and improved where you can see it. And the goal is to make our lawyers better at the work only they can do.

What You'll Do

Identify the highest-leverage AI opportunities

Spend time inside Legal teams across Match Group to understand how work happens today: product counseling, marketing reviews, contract review and negotiation, and regulatory compliance. Focus on where AI can do meaningful legal work, not just where workflows could be tidier.

Develop a clear point of view on what AI can do in a legal context today, what's coming next, and where lawyers must stay firmly in the loop.

Build the case for which opportunities are worth investing in, based on impact, feasibility, and legal risk. Prioritize across brands and teams with competing needs.

Build, prototype, and ship AI tools and agents

Design and build AI-powered tools (assistants, agents, retrieval systems, evaluators) that handle real legal work end-to-end or alongside attorneys.

Connect these tools to the data and systems they need (document repositories, knowledge bases, internal sources), working within IT, Security, and Legal Ops-governed infrastructure, not around it.

Treat shipping as the starting line: measure quality against human baselines, debug edge cases, tune prompts and pipelines, and iterate as models and the business change.

Design tools to work across brands where the legal work is shared, and flexible enough to handle brand-specific differences in products, markets, and regulations.

Train, launch, and iterate with Legal teams

Onboard attorneys and other end users to new tools and workflows, gather feedback, and make the tools useful, not just functional.

Create documentation, examples, and attorney-facing training that teach both how to use AI tools and where their judgment must remain the decision-maker. This is distinct from Legal Ops training on legal department systems.

Build evaluation harnesses and quality metrics (accuracy against human baselines, hallucination rates, coverage, latency) so you know when something is working and when it isn't.

Coordinate with Legal Operations, IT, Security, and Privacy whenever a tool touches enterprise systems or governance. They own those layers, and anything that touches them needs their involvement.

Define what AI-native legal looks like at Match Group

Partner with Legal leadership to develop a forward-looking view of an AI-native legal function and what it takes to get there.

With Legal Operations, track the state of the art in legal AI and applied AI more broadly (what frontier labs are shipping, what legal-specific tooling is emerging, what other in-house teams are building) and translate it into what's worth trying here.

Build the roadmap for how capabilities proven in one team or brand extend to others.

How Success Will Be Measured

Success here means novel legal capabilities created and adopted, not process or operational metrics.

• AI capabilities shipped that Legal teams actually use and rely on, not pilots that gather dust

• Speed and rigor of experimentation: how quickly and reliably you move from idea to evaluated prototype

• Categories of legal work meaningfully shifted from "humans only" to "AI-with-humans" or, where evidence supports it, AI-first with human audit”

• Adoption across Match Group Legal teams and brands

• Sustained quality: tools stay current, maintained, and performing as models and the legal landscape change

• Better use of Legal's attention: more attorney time on novel, high-judgment work, not just more output

Who You Are

A builder at heart. You'd rather ship a rough prototype this week than write a perfect spec for one to be built next quarter.

Equally comfortable talking with lawyers about risk and with engineers about data models, APIs, and configuration trade-offs.

Hands-on with applied AI (prompting, agents, evaluations, RAG, fine-tuning) and curious about where models are headed.

Pragmatic and business-oriented. You care less about technical purity than about shipping things that improve what Legal can do.

Energized, not frustrated, by the fact that models, tools, and best practices will keep changing.

Honest about what AI can and can't do today. You don't oversell it to lawyers or undersell it to skeptics.

A curious, respectful change agent. You move fast, but you take time to understand existing frameworks, tooling, workflows, and the teams you're working with, across brands that each have their own way of doing things.

Qualifications

No law degree required. Strong candidates for this role come from engineering and product, and from legal backgrounds where they learned to build. Either way, you'll understand how lawyers think well enough to earn their trust.

Required

• 4+ years building, deploying, or applying AI products and tools at an AI-forward tech company, a startup, a law firm innovation team, or in-house, and 2+ years building hands-on with LLMs.

• Hands-on experience with prompting, agentic workflows, retrieval-augmented generation, evaluations, and the basics of fine-tuning or model selection

• Strong technical fluency: building working systems yourself (in code or with AI coding tools like Claude Code, Codex, or Cursor), working with APIs and data, and reasoning about integrations, permissions, and guardrails

• A track record of shipping to real users, learning from how they use it, and iterating

• Excellent communication across legal, technical, and business audiences. You're a teacher who can bridge technical and plain language.

Preferred

• Experience building AI products in regulated, compliance-sensitive, or high-stakes domains (legal, healthcare, financial services, trust & safety)

• Experience as a legal engineer, legal AI engineer, legal solutions architect, legal technologist, or applied AI engineer at a law firm, tech company, or AI lab

• Experience building something new within a defined team, measuring it rigorously, and making the case for broader rollout

• Experience supporting multiple business units or brands with shared tooling