Data & ML Engineer New Remote, USA

DEFCON AI, Inc.; Northern (KY); Hybrid; USD 150,000 - 200,000; Full time

Benefits offered by this job

Fully remote work

Health insurance

Unlimited PTO

Parental leave

Job summary

DEFCON AI, Inc. seeks a Data & ML Engineer to build the data and model layer for an AI-enabled decision-support system. You’ll ingest data from multiple sources, resolve records to a shared model, compute relevance scores, and generate explanations with sources.

Responsibilities span data modeling, scoring, retrieval, and secure pipelines in a government-cloud environment. The role requires 5+ years in data/ML engineering, strong Python/SQL, and an active US Secret clearance.

Qualifications

• 5+ years in data engineering, data architecture, ML engineering, or production analytics.
• Strong Python and SQL, with large, imperfect operational data experience.
• Experience delivering systems for sustained operational use.
• Ability to explain technical decisions to non-technical stakeholders.
• Active US Secret clearance required for work in a controlled government cloud.

Responsibilities

• Design and maintain a graph of entities, records, and relationships.
• Implement probabilistic matching, blocking, scoring, and clustering.
• Develop relevance models and calibration, with abstention routing.
• Implement embeddings, vector storage, and retrieval across sources.
• Build secure ingestion, validation, and publishing pipelines; audit logging.

Skills

Data engineering

Data architecture

Applied machine learning

ML engineering

Production analytics

Python

SQL

US Secret clearance

Communication of decisions

Travel readiness

Tools

PostgreSQL

pgvector

Graph algorithms

scikit-learn

XGBoost

PyTorch

Docker

Airflow

Job description

RESILIENCE IN THE FACE OF DISRUPTION. DEFCON AI is an insights company that leverages artificial intelligence, mathematical optimization, data analytics, and software engineering for resilient optimization of complex systems.
In today’s dynamically changing world, DEFCON AI’s technology aligns outcomes with operational goals, better decision making, and empowers customers to anticipate assess, and mitigate the impacts of disruptions.

About the Role

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. That work covers ingestion from many source systems, resolution of incoming records against a shared data model, relevance scoring, and generation of explanations a user can act on and defend.

Key Responsibilities

The technical work falls into four areas. Deep expertise in all four is not expected, so please indicate where your depth lies when you apply. The engineering standards that follow apply to everyone on the team.

Data Modeling and Record Matching

• Design and maintain the graph of entities, records, and the typed relationships between them
• Implement probabilistic matching, including blocking, candidate generation, pairwise scoring, clustering, and threshold policy
• Build deduplication and known-record suppression
• Establish provenance so that every node and edge traces to the source that asserted it
• Produce interface and data-flow design documentation detailed enough to serve as an implementation reference for other engineers

Scoring and Calibration

• Develop relevance and priority models over large, imperfect record sets
• Own calibration and threshold design, establishing what a score means rather than only how it ranks
• Design abstention policy that routes uncertain and high-risk cases to a person rather than returning a confident answer
• Perform feature engineering, establish baselines before introducing complex models, and conduct error analysis that accounts for the differing cost of false positives and false negatives

Retrieval and Generation

• Implement embeddings, vector storage, and retrieval across a large provenance-tracked evidence base
• Integrate language models through an approved managed service, and maintain a self-hosted or open-weight alternative within the same boundary
• Design prompts and output schemas
• Bind generated text to cited source records, and treat "insufficient evidence" as a valid system response rather than forcing a conclusion
• Own model packaging, serving, versioning, and rollback

Pipelines and Source Handling

• Build secure ingestion, transformation, validation, and publishing across structured, semi-structured, and unstructured sources
• Implement quality checks, schema validation, lineage capture, and audit logging
• Establish source drift detection so that degradation is surfaced rather than carried into the analysis
• Generate statistically representative synthetic data so that development can proceed ahead of live data access
• Work to the data model and standards set by the Data Lead, who approves designs and owns them through customer review
• Document assumptions, caveats, transformation logic, and known limitations, since deliverables are formally reviewed
• Instrument telemetry so that measurement does not require manual reconstruction
• Maintain the audit trail covering recommendations, human overrides, and model versions
• Submit model and pipeline changes through a gated release process rather than deploying in place

Required Qualifications

• 5+ years of experience in data engineering, data architecture, applied machine learning, ML engineering, or production analytics engineering
• Strong Python and SQL, 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
• Active US Secret clearance. The work is performed in a controlled government cloud environment and requires a favorable investigation and CAC eligibility from the start
• Elevated personnel security requirements apply to portions of this work and are discussed during screening
• Willingness to travel up to 25% to customer sites, DEFCON AI HQ, and vendor facilities as required

Preferred Qualifications

• Clearance: active Top Secret
• Matching: direct experience applying probabilistic matching to inconsistent identity data, including names, dates, addresses, and identifiers, and familiarity with the failure modes of each. Record linkage, master data management, or identity management. Graph data modeling. PostgreSQL and pgvector or comparable. Graph algorithms applied in production
• Modeling: model calibration and threshold design. Cost-sensitive learning where error types carry unequal consequences. scikit-learn, XGBoost, PyTorch
• Retrieval and generation: retrieval-augmented generation in production. Prompt and output-schema design. Establishing that generated output remains grounded in its sources, and testing to confirm it. Self-hosted or open-weight model operation. Fine-tuning, adapters, or custom embeddings
• Pipelines: AWS Glue, Airflow, dbt, Spark, Kafka, or NiFi. Unstructured and semi-structured document ingestion. Synthetic or representative test data generation
• Environment: federal DevSecOps, RMF, ATO, or DoW cloud environments. Hardened base images. Experience advancing a pipeline from development through accreditation and deployment
• 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. NIST AI RMF or comparable practice

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

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

$150,000-$200,000. This represents the typical salary range for this position based on experience, skills, and other factors.

We’re an Equal Opportunity Employer: You’ll receive consideration for employment without regard to race, sex, color, religion, sexual orientation, gender identity, national origin, protected veteran status, or on the basis of disability.

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As set forth in DEFCON AI’s Equal Employment Opportunity policy,we do not discriminate on the basis of any protected group status under any applicable law.

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