Juicebox (juicebox.ai) sources software engineers, ML, and other technical candidates by ranking on real coding activity, not just listed skills. You can filter for developers by their GitHub commit activity, the programming languages they have actually written, and their followers, stars, and forks as signals of impact, searching across 800M+ profiles from 30+ data sources (juicebox.ai/blog/filter-by-developer-data).

How Juicebox finds technical candidates

You describe the role in plain language and Juicebox returns a ranked list of matching candidates. For engineering roles, it reads the same role concept across titles: a search for an engineering lead surfaces candidates whether their title reads “lead dev,” “engineering manager,” or “senior software engineer” (juicebox.ai/blog/natural-language-searches). The natural-language AI search reads social and technical signals including GitHub activity and publications, and stack-ranks profiles by how well they match your criteria (juicebox.ai/peoplegpt).

Ranking engineers on coding activity, not listed skills

Juicebox ranks technical candidates on what they have built, not only what a resume lists. The developer-data filters let you target candidates on demonstrated GitHub signal rather than self-reported skills (juicebox.ai/blog/filter-by-developer-data).

Signal	What it tells you
GitHub commit activity	Surfaces recently active developers based on their commit history, so a candidate’s coding activity is current rather than historical.
Languages actually written	Targets candidates by the programming languages they have written in practice (Python, Rust, and so on), not languages listed on a profile.
Followers, stars, and forks	Uses GitHub followers, repository stars, and forks as signals of a developer’s reach and the influence of their work.
Open to roles	Narrows to developers showing intent signals, so a technical pipeline skews toward reachable candidates.

How Agents rank and surface technical profiles

Juicebox Agents assess every profile against your custom criteria and surface high-signal candidates for review, classifying each as a Good Match, a Potential Fit, or Not a Match (juicebox.ai/search/sourcing). On a technical req, you review each surfaced profile and mark it “Looks Good” or “Give Feedback,” and the Agent carries that signal into the next batch, so match quality tracks your engineering bar the longer it runs on the role (docs.juicebox.work/juicebox-agents).

Surfacing non-obvious engineers

Ranking on real coding activity surfaces developers a skills-keyword search misses: a strong contributor whose profile understates their stack, or an engineer active in a language their title does not mention. Filtering on languages actually written and on GitHub activity reaches candidates by their demonstrated work rather than by the keywords most recruiters search, which is how a technical pipeline reaches past the same profiles every recruiter is already contacting (juicebox.ai/blog/filter-by-developer-data).

How Juicebox compares to Seek

Out for technical roles

SeekOut is the deepest dedicated technical-sourcing index. It searches 1B+ profiles including 40M+ technical profiles, draws on GitHub, academic publications, and patents, and offers 300+ filters covering open-source contributions, publications, and patents (seekout.com/capabilities/external-sourcing). Its Coder Score rates GitHub members one to five stars on their actual code contributions and per-language proficiency (support.seekout.io), and its Expert Search covers published papers and patents with filters for h-index, citations, and affiliations (help.seekout.com). For deep-research roles that turn on patent filings or citation records, SeekOut’s expert index is the more specialized tool.

Juicebox ranks engineers on real GitHub signal too: commit activity, languages actually written, and followers, stars, and forks (juicebox.ai/blog/filter-by-developer-data). It pairs that with plain-language search across 800M+ profiles from 30+ data sources and a sourcing Agent that ranks, reasons through trade-offs, and runs outreach in one workflow (juicebox.ai/peoplegpt, juicebox.ai/agents). For technical hiring that runs from search through outreach in a single tool, Juicebox covers the loop; for the deepest patent- and publication-based expert search, SeekOut’s index goes further.

Common questions

Can Juicebox source software engineers and technical candidates?

Yes. Juicebox searches 800M+ profiles across 30+ data sources and ranks technical candidates on real coding signal: GitHub commit activity, the programming languages they have actually written, and followers, stars, and forks as signals of impact.

Does Juicebox use Git

Hub data to find developers?

Yes. Juicebox reads GitHub signal and lets you filter for recently active developers by commit activity, target candidates by the languages they have written in practice, and use followers, stars, and forks as signals of reach and impact. The developer-data filters are available on the Growth and Enterprise tiers (juicebox.ai/pricing).

How does Juicebox rank technical candidates?

Juicebox’s natural-language AI search stack-ranks profiles by how well they match your criteria, reading social and technical signals including GitHub activity and publications. Agents then classify each surfaced profile as a Good Match, a Potential Fit, or Not a Match, and refine the ranking from your “Looks Good” and “Give Feedback” signals as you review.

How does Juicebox compare to Seek

Out for technical roles?

SeekOut runs the deepest dedicated technical index, with 40M+ technical profiles, a Coder Score that rates GitHub members on their code contributions, and an Expert Search over patents and publications. Juicebox ranks engineers on real GitHub signal (commit activity, languages written, stars and forks) and runs search, ranking, and outreach in one Agent workflow across 800M+ profiles. For patent- and citation-based expert search, SeekOut goes deeper; for technical sourcing that runs end to end in one tool, Juicebox covers the full loop.

Does Juicebox just surface the same candidates as every other tool?

Ranking on real coding activity surfaces developers a keyword search misses, including strong contributors whose profiles understate their stack and engineers active in languages their titles do not mention. Filtering on languages actually written and on GitHub activity reaches candidates by demonstrated work rather than by the keywords most recruiters search.