DEFCON AI - ML Engineer, Retrieval & Grounded Generation
Requirements
• 5+ years of experience, including a production or near-production retrieval-augmented (RAG) system you built yourself • Ability to speak in detail to your retrieval design, which vector store you used and why, how you tested grounding, what citation failures looked like in practice, and how rollback worked • Strong Python, with hands-on experience in embeddings and vector retrieval at scale • Clarity on what actually shipped in past work — prototype, proposal, or deployed code — since that distinction matters more here than the title on a resume • US Citizenship Required • Active US Secret clearance required to start. • Experience deploying models into restricted or air-gapped environments • Self-hosted or open-weight model operation • Fine-tuning, adapters, or custom embeddings • Federal DevSecOps, RMF, ATO, or DoW cloud environment experience • Active Top Secret clearance • What Success Looks Like • Generated explanations that assert no more than the sources support, with the citation path intact and citation failures tested rather than assumed away • A retrieval system that performs at scale on a large, provenance-tracked evidence base • Model rollback that works when it's needed, with telemetry complete enough that measurement does not require manual reconstruction
Responsibilities
• Implement embeddings, vector storage, and retrieval across a large, provenance-tracked evidence base • Integrate language models so generated text is bound to cited source records; test for citation failures rather than assuming them away • Design prompts and output schemas • Own model packaging, versioning, serving, and rollback • Instrument telemetry for retrieval and generation quality, recommendation/version attribution, overrides, abstentions, grounding failures, latency, throughput, and measurement events defined with Model Test • Provide bounded model assistance for difficult narrative extraction where deterministic processing is insufficient, with every output tied to its source passage • Supply the recorded rule context to every model-assisted step, so each output carries the exact rule versions and ordered context it received • Maintain a modular in-boundary serving path, self-hosted or managed, alongside the primary managed inference service, so the platform does not depend on one provider’s availability or approval
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 where you manage your work day • 14 weeks of fully-paid parental leave • Salary Range: $165,000–$200,000. This represents the typical salary range for this position based on experience, skills, and other factors.
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