TBPN · 4 min · Sep 26, 2025

Recruiting search improves when it looks beyond LinkedIn

David Paffenholz · Co-founder & CEO, Juicebox

Juicebox uses role-specific data, including GitHub for engineers and customer profiles for sales hires, to find candidates recruiters might miss.

In this episode

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1:51:35 Delian favors AI leaders and businesses insulated from the cycle6 min
2:20:38 Flock's product expansion is outpacing customer adoption6 min
48:00 Radiology is a poor proxy for near-term AI job losses5 min
2:37:39 Enterprise coding agents need to cover the full workflow5 min
2:57:37Recruiting search improves when it looks beyond LinkedIn4 min

Up next · All-InPalantir says its safeguards make data analytics harder to abuse6:30

“GitHub profiles, a ton of value on those, especially where, you know, traditional LinkedIn profiles or resumes might be a bit more sparse”

David Paffenholzat 3:00:02 · TBPN

Takeaways

The product focuses on finding new candidates, not managing the whole hiring pipeline.
GitHub profiles can reveal engineers with sparse resumes.
Sales sourcing can use information about the buyers a candidate's employer serves.

About this segment

Juicebox uses role-specific data, including GitHub for engineers and customer profiles for sales hires, to find candidates recruiters might miss. It focuses on sourcing rather than pipeline management.

David Paffenholz

Co-founder & CEO, Juicebox

Co-founded Juicebox, an AI recruiting platform that helps teams search, evaluate, and reach candidates.

John Coogan, Host · Co-host, TBPN
Co-founded Soylent and Lucy; OpenAI bought his show TBPN in April 2026.
Jordi Hays, Host · Co-host, TBPN
Founded Branded Native; co-founded fintech Capital, which Rho bought.

From “Thursday, September 25th”, Sep 26, 2025. Watch the full episode

Transcript

From the transcript TBPN publishes with the episode.

2:57:50
Yeah. It's a great question. I think of the the recruiting space as really having two different ways in which you could add value. One is finding net new candidates, people that you wouldn't have discovered or wouldn't have gotten into your pipeline in the first place, and then everything that's carrying through the pipeline and optimizing that pipeline. And the latter is usually about, like, saving time and optimizing conversion rates, And the former is about making sure the right talent is speaking to you in the first place.
2:58:15
We're focused on that part. And so our goal is to help companies discover talent that they wouldn't otherwise. And we do that by combining a bunch of different data sources and then using large language models to run the search and surface those profiles for you. And so if you think about what a, like, recruiter does today on, say, LinkedIn recruiter or, a typical search solution, they set a bunch of filters and then start reviewing profiles one by one. It's super manual, requires a lot of thought still to like think of what could make that profile a fit.
2:58:45
And that's exactly the process that we're able to do with with LLMs. So is this a direct replacement for LinkedIn Recruiter? Are you are you trying to trying to get people to just be able to
2:58:56
Or do you wanna, like, puppeteer LinkedIn with a screen recorder or API or something like widget don't if they'd like like that. I don't know. It depends. I mean, there's been some companies that have done it successfully. Some of them got acquired by LinkedIn. But I don't know.
2:59:09
Yeah. No puppeteering of LinkedIn. Most of our customers are existing LinkedIn customers. Sure. Our goal is to help you find net new talent and do so in a more efficient way. So it's really about kind of enhancing the workflow of the recruiter and enabling them to do that search that would just be too manual or too time consuming to do otherwise. Like, looking through 50,000 software engineering profiles is just not something that's feasible. But for an LLM, it's very feasible and can do it in a couple minutes.
2:59:38
So there are I mean, are different pools of talent for I mean, we do media video production here. We might want to hire somebody who doesn't really have a LinkedIn presence, but they might have an Instagram account or they might have a personal website where they've done some, they put up their show reels or what they've worked on. Do you have crawlers that could go out and find those profiles on the Internet? Like, how could I think about the surface area of you going out and finding me candidates and sourcing?
3:00:06
That that's right. So we look at a a bunch of different data sources for different types of roles. So Mhmm. I'll give two examples. One on the engineering side, you know, GitHub profiles, a ton of value on those, especially where, you know, traditional LinkedIn profiles or resumes might be a bit more sparse, especially for for engineers who don't put a ton of info on there, GitHub's can be really rich and a really interesting data source.
3:00:28
Meanwhile, on the sales side, often what we see is, you know, customers want to find sellers who have experience selling it to specific type of buyer persona. And so there, it's actually a bit more about the company data. You know, what type of companies does their current company sell into? What type of a platform are they selling? Is it a SaaS product? Is it a usage based product and more? And so all of that information we enrich and we try to aggregate upfront so we can make those decisions for you and help surface the right profiles there.
3:00:55
There's another they put an example. They have a tag on their site called likely to switch, which would be like you have round of layoffs, Oh, Sure. Funding or vesting timelines. You know, people don't really think about that. You can see like, okay, this person's been here for four years.