Data & ML Engineer

Red Cell Partners

1d•$150,000 - $200,000•Remote

About The Position

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

Requirements

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
US Citizenship Required
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

Nice To Haves

Clearance: active Top Secret
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
Model calibration and threshold design. Cost-sensitive learning where error types carry unequal consequences. scikit-learn, XGBoost, PyTorch
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
AWS Glue, Airflow, dbt, Spark, Kafka, or NiFi. Unstructured and semi-structured document ingestion. Synthetic or representative test data generation
Federal DevSecOps, RMF, ATO, or DoW cloud environments. Hardened base images. Experience advancing a pipeline from development through accreditation and deployment
Sensitive federal or defense data, and work performed under privacy or comparable handling constraints
Documentation, model cards, fairness testing, and model monitoring. NIST AI RMF or comparable practice

Responsibilities

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
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
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
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

Benefits

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
Career track opportunity with potential for rapid advancement with strong performance as the firm grows
Opportunities for professional development and continued learning
Optional 401K, FSA, and equity incentives available
Mental health benefits are available through Tara Mind
Cost effective GLP-1 solutions available through Crux