[Hiring] Data & ML Engineer @DEFCON AI

Role Description

As a Data & ML Engineer you will build the data and model layer behind an AI-enabled decision-support system operating inside an accredited environment. Your work will cover:

• Ingestion from many source systems
• Resolution of incoming records against a shared data model
• Relevance scoring
• Generation of explanations a user can act on and defend

Three characteristics make this a substantial technical challenge:

• The incoming data is predominantly low-signal, meaning a model can report strong overall accuracy while failing on the cases that matter most.
• Every output must remain traceable to the underlying sources, as a person downstream is accountable for the result.
• Record matching is probabilistic rather than exact, so false matches and missed matches both carry meaningful cost.

You will not be starting from an empty repository. We operate an established platform for source custody, extraction, and retrieval, and its architect is a member of this team. Your work will focus on new capability rather than maintenance:

• Record matching
• Calibrated scoring
• Grounded generation, hardened for the target environment

This is a fully remote role with occasional travel (up to 25%) to DEFCON AI HQ, customer sites, and vendor facilities as required.

Qualifications

• 5+ years of experience in data engineering, data architecture, applied machine learning, ML engineering, or production analytics engineering
• Strong Python and SQL skills, with demonstrated experience working with large, imperfect operational data
• Experience delivering systems for sustained operational use rather than exploratory analysis alone
• Routine use of AI-assisted development, with informed judgment about where it adds value and where its output requires verification
• Ability to explain a technical decision to a stakeholder who must defend that decision without understanding its internals
• US Citizenship Required
• Active US Secret clearance
• Willingness to travel up to 25% to customer sites, DEFCON AI HQ, and vendor facilities as required

Requirements

• Clearance: active Top Secret
• Matching: direct experience applying probabilistic matching to inconsistent identity data, including names, dates, addresses, and identifiers
• Modeling: model calibration and threshold design, cost-sensitive learning where error types carry unequal consequences
• Retrieval and generation: retrieval-augmented generation in production, prompt and output-schema design
• Pipelines: AWS Glue, Airflow, dbt, Spark, Kafka, or NiFi
• Environment: federal DevSecOps, RMF, ATO, or DoW cloud environments
• Domain: sensitive federal or defense data, and work performed under privacy or comparable handling constraints
• Responsible AI: documentation, model cards, fairness testing, and model monitoring

Benefits

• 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

What Success Looks Like

• A data model that the rest of the team builds on without needing to redesign it
• Matching decisions that can be explained and defended to a non-technical reviewer
• Models whose miss rate is characterized, not only their overall accuracy
• Generated explanations that assert no more than the sources support, with the citation path intact
• Pipelines that surface problems early and trace them to a specific source
• Consistent development progress, including during periods when live data is not yet available