Data & ML Engineer
$150k - $200kFull-time
Red Cell Partners
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
Vacancy posted 1 day ago
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