Senior Entity Resolution Engineer
Role Overview
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.
Key Responsibilities
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
Key Skills for This Role
Full Job Posting
About the Role
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.
Key Responsibilities
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
Required Qualifications
- 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 Qualifications
- 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
- A data model and matching pipeline the rest of the team can build on without redesigning it
- Provenance intact end-to-end, so every match traces to its source
- What We Offer
- A fully remote, results-based environment
- Competitive salary, bonus, and equity package
- 100% employer paid, comprehensive health insurance including medical, dental, and vision for you and your family
- Unlimited PTO, with your manager’s approval
- Flexible work environment where you manage your work day
- 14 weeks of fully-paid parental leave
- Salary Range: $160,000—$195,000. This represents the typical salary range for this position based on experience, skills, and other factors.
- Managing and administering your application throughout the hiring process;
- Verifying the accuracy and authenticity of application materials, including by cross-referencing information you provide against publicly available sources and proprietary databases;
- Identifying indicators of potentially fraudulent, fabricated, or materially misleading application content, including but not limited to discrepancies between submitted materials and publicly available professional profiles, geographic anomalies, and fabricated work histories.
About DEFCON AI
Private defense technology company providing AI-powered modeling, simulation, and optimization software for military and enterprise operations.
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