ML Engineer, Retrieval – Grounded Generation

Job not on LinkedIn

🔥 4 minutes ago

🇺🇸 United States – Remote

💵 $165k - $200k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 0%

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Defcon AI

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

🚗 Transport

📦 Logistics

Artificial Intelligence • Transport • Logistics

Defcon AI is a company focused on transforming logistics and supply chain operations using AI-driven technologies. The company addresses disruptions caused by natural disasters, unanticipated events, and opponents through sophisticated software modeling and intelligent agents. Defcon AI aims to integrate next-generation technologies within logistics and decision-making processes to improve response planning in complex and contested environments. Positioned at the convergence of AI, mobility, and logistics, Defcon AI collaborates with partners to provide efficient, reliable, and data-driven solutions tailored to specific needs. The company is committed to enhancing resilience and efficiency in the logistics sector.

📋 Description

• Build embeddings, vector storage, and retrieval at scale across a large, provenance-tracked evidence base • Integrate language models so generated text is bound to cited source records • Test citation failures and grounding quality • 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 • Provide bounded model assistance for difficult narrative extraction, with every output tied to its source passage • Supply recorded rule context to every model-assisted step, including exact rule versions and ordered context • Maintain a modular in-boundary serving path, self-hosted or managed, alongside the primary managed inference service • Deliver generated explanations, scalable retrieval, reliable rollback, and complete measurement telemetry

🎯 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 • Preferred: Experience deploying models into restricted or air-gapped environments • Preferred: Self-hosted or open-weight model operation • Preferred: Fine-tuning, adapters, or custom embeddings • Preferred: Federal DevSecOps, RMF, ATO, or DoW cloud environment experience • Preferred: Active Top Secret clearance

🏖️ 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