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Salary: USD 160,000 - 195,000 / annual
DEFCON AI is an insights company leveraging artificial intelligence, mathematical optimization, data analytics, and software engineering to optimize complex systems for resilience and better decision-making in the face of disruption.
You will own entity resolution—one of the most critical parts of the platform. This role turns in-house graph design and matching strategy concepts into production-ready capability. You are a builder responsible for implementation details across the entire matching lifecycle: blocking strategies, candidate generation, pairwise scoring, clustering, threshold policy, deduplication, and known-entity checks.
Key responsibilities include:
- 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, enabling auditability and explainability
- Own the false-merge versus false-split tradeoff in matching decisions and make it explainable to stakeholders
- Own technical execution of the matching approach against the in-house design: 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
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. You'll join a team building technology that supports real-world government mission needs. Every match, merge, and relationship you create helps turn fragmented information into insight.
This is a fully remote role with occasional travel to DEFCON AI headquarters, customer sites, and partner facilities as needed.
**Requirements:**
- 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
