Defcon Senior Entity Resolution Engineer at Defcon AI builds production-ready entity resolution capabilities for a critical platform supporting government missions. This role owns the matching lifecycle and provenance framework.
Responsibilities
This role sits in one of the most critical parts of the platform: determining when records from dozens of disparate sources represent the same real-world entity. Graph design and matching strategy are developed in-house, and you’ll be the engineer who turns those concepts into production-ready capability. You’ll shape implementation details, but this is first and foremost a builder role.
You’ll join a team building technology that supports real-world government mission needs. The platform ingests and analyzes data from a wide range of sources, applies AI-assisted workflows to surface what matters most, and provides transparent, explainable recommendations that analysts can trust. Every match, merge, and relationship you create helps turn fragmented information into insight.
You’ll own the matching build lifecycle end to end: blocking strategies, candidate generation, pairwise scoring, clustering, threshold policy, and the deduplication and known-entity checks that reuse the same engine. You’ll also build the provenance framework that lets every node, edge, and assertion in the graph be traced to the source that asserted it, so matching decisions are auditable and explainable.
This is a fully remote role with occasional travel to DEFCON AI headquarters, customer sites, and partner facilities as needed.
Implement and refine entity-level record matching: blocking, candidate generation, pairwise scoring, clustering, and threshold policy
Reuse one matching engine for record linkage, deduplication, and known-entity checks
Establish provenance so every node and edge traces back to the source that asserted it
Own the false-merge versus false-split tradeoff in matching decisions and make it explainable
Own technical execution of the matching approach against the in-house design: the fixed reference dataset, candidate retrieval and final matching evaluated separately, and threshold recommendations with evidence for review
Document matching logic in enough detail to serve as an implementation reference for other engineers
Qualification
5+ years of experienceStrong Python and SQLUS Citizenship RequiredRecord linkagePostgreSQL and pgvector or comparableActive Top Secret clearanceWhat Success Looks Like
Required
5+ years of experience, including shipping production record matching or entity resolution
Ability to explain the matching tradeoffs you made, including how you handled false merges versus false splits
Strong Python and SQL, with demonstrated experience on large, messy, real-world data
Ability to explain a matching decision to a stakeholder who must defend it without understanding its internals
US Citizenship Required
Active US Secret clearance required to start
Preferred
Direct experience applying probabilistic matching to inconsistent identity data such as names, dates, addresses, and identifiers, and familiarity with the failure modes of each
Familiarity with probabilistic record-linkage frameworks and tooling such as Fellegi-Sunter models, Splink, Dedupe, Zingg, or an in-house equivalent
Record linkage, master data management, or identity management experience
Graph data modeling and graph algorithms applied in production
PostgreSQL and pgvector or comparable
Active Top Secret clearance
What Success Looks Like
Matching decisions that can be explained and defended to a non-technical reviewer
