Have you got a enormous number of technical CVs to filter through? Our solution provides fast filtering (often filtering 1000s of CVs in minutes) and presents back to you the key CVs which matches your deep tech job description.
CVranker by scientometrics.ai is a high-performance CV screening and ranking engine designed for the demands of deep tech recruitment. Built on top of proprietary domain-specific CV encoders, CVranker enables organizations to quickly filter and prioritize thousands of technical CVs against detailed job descriptions or research criteria. It goes far beyond keyword matching, using semantic understanding of content to evaluate candidate fit in fields like artificial intelligence, robotics, quantum computing, and advanced software systems.
Traditional applicant tracking systems (ATS) rely on surface-level heuristics - such as presence of keywords or job titles - that are often inadequate for complex research and engineering roles. In contrast, CVranker uses neural models fine-tuned on deep tech resumes and job descriptions to capture latent skill patterns, research contributions, and project relevance. The underlying models are tailored to recognize context-specific terms, technical hierarchies, and domain-specific phrasing, allowing for true semantic scoring of candidate profiles.
The importance of semantic CV matching is well established. Research in natural language processing (e.g., Guo et al., EMNLP, 2019) shows that deep learning-based encoders outperform traditional methods in capturing nuanced skill-job alignment, especially when domain specificity is high. CVranker brings this capability to hiring in cutting-edge R&D fields, where standard HR systems lack the resolution to distinguish, for example, a reinforcement learning expert from a generalist machine learning engineer.
Workflows:
CVranker supports two key workflows: (1) job-matching mode, where a job description is ingested and all CVs are scored against it; and (2) standalone analysis mode, where individual CVs are parsed to extract structured insights such as skills, affiliations, publication history, and project experience. This flexibility allows companies to use the tool not only for automated shortlisting, but also for building talent intelligence databases or populating internal knowledge graphs with skill taxonomies.
Designed for speed and scale, CVranker can process thousands of CVs within minutes, returning ranked shortlists with associated confidence scores and skill breakdowns. This is especially valuable for VC-backed startups and enterprise R&D teams facing inbound application surges or participating in mass hiring events.
In summary, CVranker transforms manual screening into an automated, explainable, and domain-specific workflow for technical hiring. It enables organizations to uncover high-potential candidates who might otherwise be missed - bringing scientific rigor to talent discovery in the world’s most competitive technology sectors.