Uplevyl
Website:
uplevyl.com
Job details:
About Uplevyl
Uplevyl builds AI-powered knowledge and community infrastructure for organizations serving women. Our products include UpGenie (our domain-specific AI assistant), WeHub (our community platform), and UpSocial (a social platform for women). We work with mission-driven partners to turn complex, high-stakes information into clear, trustworthy guidance, and to build systems that create lasting impact.
We hold a simple conviction: in high-stakes domains like rights, law, and financial security, a generic AI is not enough. The answers people stake their lives and livelihoods on need a purpose-built system with verified, native data. That is what we build, and it is why the work is urgent. AI is reshaping how the world learns, works, and earns, and the women we serve cannot afford to be left off that train.
We have also made a deliberate choice about how we build: a small team of exceptional people, paid well above market, each doing work that would normally take several. We would rather be ten people who move the world than thirty who move paper. That choice sets the bar for every hire, including this one.
About the role
Our products answer questions people stake their livelihoods on. When someone asks whether she can take leave after an assault, whether her employer is allowed to demand documentation, or what her state actually requires, the answer has to be right: right about the law, right about her jurisdiction, and honest about what it does not know. This needs building the system that measures whether we are right, catches us when we are not, and improves the model on evidence. That system is what you own.
You will sit between our law and our engineering and be fluent in both. Our AI engineers run build sprints. You run evaluation sprints alongside them, in a loop: they ship a change, and you tell them, with numbers, whether it made the product better or worse and where it broke. You will also do the legal work itself, verifying sources, defining what a good answer looks like in a given domain, and making the scope calls about what we cover and what we deliberately do not.
We currently cover US workplace rights across state, county and city jurisdictions. It is not an advisory or litigation practice, you will not be giving legal advice or appearing for clients, and US bar admission is not required. What is required is that you can read US employment statutes with a practitioner’s eye and know when a source is wrong, stale, or wrongly scoped.
What you'll own
- The evaluation framework. Today we have no defined way to prove that version two of a model is better than version one. Build it: the benchmark sets, the scoring rubrics, the grading criteria, and the pipeline that runs them. Decide what correct means for a legal answer, across accuracy, jurisdictional fit, citation integrity and honesty about uncertainty, and make every one of those measurable.
- The golden datasets. Curate the query sets and verified answers we test against, jurisdiction by jurisdiction and law type by law type. These are the ground truth for everything else we measure.
- The improvement loop with engineering. Sit inside the sprint cycle with our AI engineers. Every model or retrieval change gets evaluated before and after, and every regression gets traced to a cause. You are the reason we know whether our model is getting better.
- The knowledge layer itself. Own the legal substance underneath the product: source verification, metadata quality, definitions, and the scope calls about what a resource covers. Where a source is ambiguous, you make the call and you write down why.
- Legal judgment in the room. Be the lawyer this team can turn to. When our product people are shaping a new vertical, when our engineers are writing guardrails, when a partner asks something none of us can answer, you are the one who knows or knows how to find out fast.
- Public credibility for our accuracy. We intend to publish benchmark results comparing our performance against general-purpose models. You own the methodology behind those numbers, which means it has to survive scrutiny from other benchmarking bodies.
Who we're looking for
We are not looking for a lawyer who is curious about AI, or for an engineer who has read some employment law. We are looking for someone who has genuinely done both.
- A practising lawyer's depth in employment law. A law degree and real practice or research experience in employment, labour or workplace rights. You read statutes closely for a living, and you can tell a well-drafted provision from a badly drafted one.
- You have already built with AI. You have stood up something that works: an evaluation harness, a retrieval pipeline, a scoring script, a structured dataset at scale. You can write enough code to do this yourself, and you understand where these systems fail.
- Evaluation instinct. You think in terms of what would falsify a claim. Given a model output, you can say precisely what is wrong with it, why it is wrong, and what test would have caught it earlier.
- Comfort with ambiguity in the law. Much of this domain has no clean answer. You are willing to make a defensible call, record your reasoning, and revisit it when the evidence changes.
- Sharp writing. You can explain a jurisdictional subtlety to an engineer and an evaluation result to a non-technical partner, in a few clear lines each.
- High agency. You unblock yourself. Nobody here has done this job before, so there is no playbook waiting for you. You write it.
- Direct, clear communication. You will sit in front of partners and clients as our legal voice. You need to hold that room, take a hard question, and answer it plainly.
Even better if
- You have worked on legal AI, legal tech, or an AI product in another regulated domain.
- You have experience with US employment law specifically, or with multi-jurisdictional legal research at scale.
- You have published research, benchmarks, or writing on AI evaluation.
- You have worked in mission-driven, social-impact, women-focused, or nonprofit ecosystems.
- You have founder, startup operator experience.
Before you apply
We want to be direct about what this is. You will spend as much time in datasets, rubrics and evaluation runs as you will in statutes, and if that combination does not appeal to you, this is not the right fit. It is also a role of one, on a small senior team, at a company where the accuracy of what we tell people matters more than almost anything else we do. The work is demanding, the expectations are real, and the rewards, in compensation, in ownership, and in what you will learn, match them. If you want to define how a legal AI system proves that it can be trusted, we want to hear from you. Our interview process is thorough, and we will walk you through every step of it in our first conversation.
How we work at Uplevyl
- We finish what we start, and we do it well. We follow through on what we say we'll do, and we care about the difference our work makes.
- We listen before we decide. Before we act, we ask who it actually affects: a customer, a partner, a teammate, or the communities we serve. Trust here is built the plain way, by consistently showing up for people.
- We move with ownership and urgency. We don't wait for perfect information or for someone else to raise their hand. If something looks like it could go wrong, we say so early. We make the call, we move fast, and we hold a high bar for quality.
- We are better together than alone. We work across teams, say what we actually think, and go out of our way to help each other succeed. Leadership here is measured by the impact you create.
- We stay curious and keep learning, including how to use AI well. We hold ourselves to the bar we're building toward: questioning our own assumptions and using AI to think better and move faster.
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