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.Three characteristics make this a substantial technical challenge. The incoming data is predominantly low-signal, which means a model can report strong overall accuracy while failing on the cases that matter most. Every output must remain traceable to the underlying sources, because 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, so existing design decisions are documented and accessible. Your work will focus on new capability rather than maintenance: record matching, calibrated scoring, and grounded generation, hardened for the target environment. We build with current tooling and expect the same, including the use of AI assistance in our own engineering practice.This is a fully remote role with occasional travel (up to 25%) to DEFCON AI HQ, customer sites, and vendor facilities as required.Key ResponsibilitiesThe 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 MatchingDesign and maintain the graph of entities, records, and the typed relationships between themImplement probabilistic matching, including blocking, candidate generation, pairwise scoring, clustering, and threshold policyBuild deduplication and known-record suppressionEstablish provenance so that every node and edge traces to the source that asserted itProduce interface and data-flow design documentation detailed enough to serve as an implementation reference for other engineersScoring and CalibrationDevelop relevance and priority models over large, imperfect record setsOwn calibration and threshold design, establishing what a score means rather than only how it ranksDesign abstention policy that routes uncertain and high-risk cases to a person rather than returning a confident answerPerform feature engineering, establish baselines before introducing complex models, and conduct error analysis that accounts for the differing cost of false positives and false negativesRetrieval and GenerationImplement embeddings, vector storage, and retrieval across a large provenance-tracked evidence baseIntegrate language models through an approved managed service, and maintain a self-hosted or open-weight alternative within the same boundaryDesign prompts and output schemasBind generated text to cited source records, and treat "insufficient evidence" as a valid system response rather than forcing a conclusionOwn model packaging, serving, versioning, and rollbackPipelines and Source HandlingBuild secure ingestion, transformation, validation, and publishing across structured, semi-structured, and unstructured sourcesImplement quality checks, schema validation, lineage capture, and audit loggingEstablish source drift detection so that degradation is surfaced rather than carried into the analysisGenerate statistically representative synthetic data so that development can proceed ahead of live data accessEngineering StandardsWork to the data model and standards set by the Data Lead, who approves designs and owns them through customer reviewDocument assumptions, caveats, transformation logic, and known limitations, since deliverables are formally reviewedInstrument telemetry so that measurement does not require manual reconstructionMaintain the audit trail covering recommendations, human overrides, and model versionsSubmit model and pipeline changes through a gated release process rather than deploying in placeRequired Qualifications5+ years of experience in data engineering, data architecture, applied machine learning, ML engineering, or production analytics engineeringStrong Python and SQL, with demonstrated experience working with large, imperfect operational dataExperience delivering systems for sustained operational use rather than exploratory analysis aloneRoutine use of AI-assisted development, with informed judgment about where it adds value and where its output requires verificationAbility to explain a technical decision to a stakeholder who must defend that decision without understanding its internalsUS Citizenship RequiredActive US Secret clearance. The work is performed in a controlled government cloud environment and requires a favorable investigation and CAC eligibility from the startElevated personnel security requirements apply to portions of this work and are discussed during screeningWillingness to travel up to 25% to customer sites, DEFCON AI HQ, and vendor facilities as requiredPreferred QualificationsClearance: active Top SecretMatching: 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 productionModeling: model calibration and threshold design. Cost-sensitive learning where error types carry unequal consequences. scikit-learn, XGBoost, PyTorchRetrieval 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 embeddingsPipelines: AWS Glue, Airflow, dbt, Spark, Kafka, or NiFi. Unstructured and semi-structured document ingestion. Synthetic or representative test data generationEnvironment: federal DevSecOps, RMF, ATO, or DoW cloud environments. Hardened base images. Experience advancing a pipeline from development through accreditation and deploymentDomain: sensitive federal or defense data, and work performed under privacy or comparable handling constraintsResponsible AI: documentation, model cards, fairness testing, and model monitoring. NIST AI RMF or comparable practiceWhat Success Looks LikeA data model that the rest of the team builds on without needing to redesign itMatching decisions that can be explained and defended to a non-technical reviewerModels whose miss rate is characterized, not only their overall accuracyGenerated explanations that assert no more than the sources support, with the citation path intactPipelines that surface problems early and trace them to a specific sourceConsistent development progress, including during periods when live data is not yet availableWhat We Offer: A fully remote, results-based environmentCompetitive salary, bonus, and equity package100% employer paid, comprehensive health insurance including medical, dental, and vision for you and your familyUnlimited PTO, with your manager’s approvalFlexible work environment where you manage your work day14 weeks of fully-paid parental leaveSalary Range: $150,000-$200,000. This represents the typical salary range for this position based on experience, skills, and other factors.

Data & ML Engineer

Emma

Responsibilities

Skills wanted:

Language(s) required:

About DEFCON AI:

www.defconai.com/

Admin access needed