Data & ML Engineer

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

Key challenges include:

• Incoming data is predominantly low-signal
• Every output must remain traceable to the underlying sources
• Record matching is probabilistic rather than exact

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
• Experience delivering systems for sustained operational use
• Routine use of AI-assisted development
• Ability to explain a technical decision to a stakeholder
• US Citizenship Required
• Active US Secret clearance
• Willingness to travel up to 25%

Requirements

• Clearance: active Top Secret (preferred)
• Direct experience applying probabilistic matching to inconsistent identity data
• Familiarity with graph data modeling and PostgreSQL
• Experience with model calibration and threshold design
• Experience with AWS Glue, Airflow, dbt, Spark, Kafka, or NiFi
• Experience in federal DevSecOps environments

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
• 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
• 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