“One accurate measurement is worth a thousand expert opinions.”
Grace Hopper
Grace Hopper had a point. But what if you’re the rare person who brings both: the measurements and the opinions?
That’s exactly who this role is for. Our client is one of the fastest-growing AI data companies in the world, a unicorn building the datasets and evaluations that sit behind today’s most capable models. Nearly every frontier lab is a customer. The team is still under 100 people, the founders came out of elite quant trading and big tech, and the growth curve points almost straight up.
They’re hiring two Research Partnerships Leads in San Francisco, and it’s one of the more unusual roles we’ve worked on this year.
The role in one line
You’re the researcher on the commercial team.
The people who buy frontier data are researchers inside AI labs. They have no patience for a pitch; they want a peer. They want someone who has stared at model outputs long enough to know why an assistant feels sharp for one user and useless for another, and who can back that view with evidence.
Your job is to hold those views, sharpen them, and turn them into proposals, analyses and research collateral that land on a lab researcher’s desk and earn the reaction every vendor wants: “these people actually get it.” The aim is for labs to treat our client as a thinking partner well before any data changes hands.
You’ll start behind the scenes, writing and building alongside the commercial team. Over time you’ll step in front of lab clients yourself. With only two hires to begin with, you won’t inherit a function. You’ll shape one.
What you’ll actually do
- Decide what good looks like. Form clear, defensible views on how a personal assistant, an enterprise agent or a general chatbot should behave for different kinds of users.
- Write things researchers finish reading. Proposals and analyses that show where models fall short and which data would close the gap.
- Build the evidence. Run the experiments and pull the examples that turn an opinion into an argument.
- Present it live. First to the internal team, then to rooms full of lab researchers who will ask hard questions.
- Listen closely. You’ll hear first-hand how different labs think about model behavior, evals and data, and feed that back into what gets built next.
The scorecard: how you’ll be assessed
Most adverts make you guess what the hiring team cares about. We’d rather show you. Every candidate for this role is measured against the same scorecard, and here it is in plain English.
The four essentials
All four matter. If you can tick each one, we want to hear from you today.
- You’ve worked where the frontier is. That means time at a frontier or neo lab, or at an AI evals or data company. Think OpenAI, Anthropic, Scale AI, Snorkel AI, METR or Redwood Research, and their peers.
- Your model behavior or interpretability work was hands-on. Post-training, evals, answer quality, interpretability methods: the label matters less than the fact that you did the work yourself. You read the outputs, designed the experiments and drew the conclusions.
- You write and present with real clarity. The test is simple. Could you write a proposal that a lab researcher would find sharp and credible, then stand up and present it live to a room? Both halves count.
- You can be in the office five days a week. Monday to Friday in San Francisco’s Financial District. If you’re not in the Bay Area yet, relocation support is on the table.
The baseline
- Roughly a year or more of model behavior, interpretability or evals work. Quality beats tenure here. Eight strong months on model behavior inside a frontier lab counts for more than several years somewhere adjacent.
- Strong, specific opinions. “It depends” won’t carry you far. You should be able to say what makes a model good or bad for a particular user or use case, explain why, and defend it when someone pushes back.
What makes you stand out
- You’ve contributed to benchmarks, datasets or evaluation projects, or you’ve worked with human annotator and expert pools.
- You hold a technical degree in computer science, machine learning, statistics, data analytics, economics or similar. Equivalent hands-on experience in ML, software or quantitative work is every bit as welcome.
Where the fit gets harder
In the spirit of full transparency, two backgrounds tend to fall outside what the team is looking for:
- Product-facing roles that never involved hands-on model work.
- AI experience gained solely at a large cloud provider, with no frontier or neo lab chapter alongside it. Pair that experience with time at a lab and you’re firmly back in the picture.
How to use this
When you apply, speak directly to the scorecard. Tell us which lab or evals company you worked at, what you personally did on model behavior, and point us to something you’ve written or presented. Add one opinion about model behavior that you’d happily defend in a room of researchers. That’s the application that gets noticed.
Why this one is worth your attention
- Reach. Inside a single lab, your thinking influences one family of models. Here it travels across almost the entire frontier at once.
- A front-row seat. You’ll learn how researchers at different labs really think about behavior, evals and data. Very few seats in the industry offer that view.
- Serious backing. You’ll have one of the deepest ready-made data catalogs in the business behind every proposal you write.
- A blank page. Two founding hires, a direct line to the leader building the function, and room to grow it as far as your ideas take it.
- Proper reward. Up to $300K base, on-target earnings up to $500K, and competitive equity in a company whose valuation has already passed the billion-dollar mark.
The details
- Location: San Francisco, Financial District
- Work pattern: On-site, Monday to Friday
- Base salary: Up to $300K
- On-target earnings: Up to $500K
- Equity: Competitive
- Relocation: Support available
- Work authorization: Existing US work authorization strongly preferred
- Openings: 2
Big Wave Digital: your true partners in AI and tech roles
Big Wave Digital is a specialist recruitment firm focused on AI and technology. We keep our desk deliberately small so that every candidate deals with a person who understands the work, knows the client and has read the scorecard before reading your resume.
That means you get the full picture from us: how the team evaluates, where you’re strong, and how to present your experience so it lands. We prepare you properly, we tell you where you stand, and we stay in your corner from first conversation to signed offer. Recruitment should feel like a partnership, so that’s how we run it.
Ready?
If you’ve spent your days working out why models behave the way they do, and you have opinions you’re itching to put in front of the people building them, apply through the form on this page. Tell us where you’ve worked, what you did with your own hands, and the one view on model behavior you’d defend to anyone.
