ML Engineer, Retrieval & Grounded Generation

DEFCON AI; Oregon; Híbrido; USD 165.000 - 200.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Fully remote work

Health insurance for you and family

Unlimited PTO

14 weeks of parental leave

Equity package

Bonus

Descripción de la vacante

DEFCON AI is building an AI-assisted platform that ingests records from multiple feeds, resolves them to the correct person, and explains recommendations with citations in a secure government cloud. You will own retrieval design, embeddings, and model lifecycle in a fully remote setting.

The role emphasizes scalable retrieval systems, provenance tracking, and bounded outputs tied to source passages, with strong emphasis on grounding and explainability.

Formación

• 5+ years of production or near-production retrieval-augmented (RAG) system work you built yourself.
• Ability to discuss retrieval design, which vector store you used and why, testing grounding, and how rollback works.
• Strong Python skills with hands-on experience in embeddings and vector retrieval at scale.
• Clarity on what actually shipped in past work — prototype, proposal, or deployed code.
• US Citizenship required and Active US Secret clearance to start.

Responsabilidades

• Implement embeddings, vector storage, and retrieval across a large provenance-tracked evidence base.
• Integrate language models with binding to cited source records; test for citation failures.
• Design prompts and output schemas.
• Own model packaging, versioning, serving, and rollback.
• Instrument telemetry for retrieval and generation quality, throughput, and measurement events.
• Provide bounded model assistance with outputs tied to source passages.
• Supply the recorded rule context to every model-assisted step with exact rule versions.
• Maintain a modular in-boundary serving path, self-hosted or managed, alongside the primary service.

Conocimientos

RAG architecture design

Python

Embeddings & vector retrieval

Grounding and explainability

Model rollback & provenance

Herramientas

Vector store selection

Secure deployment in gov cloud

Descripción del empleo

ABOUT DEFCON AIRESILIENCE 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

You’ll join the analytics and AI engineering team behind a system that genuinely matters: an AI-assisted platform that pulls together records from dozens of external feeds, resolves them to the right person, surfaces what a human reviewer should look at first, and explains every recommendation in plain, defensible terms — running inside a secure government cloud environment. It's the kind of problem where the details you get right are the ones that count, which is exactly what makes it worth doing well.

Key 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

Required Qualifications

• 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 Qualifications

• 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

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: $165,000-$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.

Applicant Data Disclosure

• Managing and administering your application throughout the hiring process
• Verifying the accuracy and authenticity of application materials, including by cross-referencing information you provide against publicly available sources and proprietary databases
• Identifying indicators of potentially fraudulent, fabricated, or materially misleading application content, including but not limited to discrepancies between submitted materials and publicly available professional profiles, geographic anomalies, and fabricated work histories

Applications that are flagged through this process as containing indicators of fraud or material misrepresentation may be declined from further consideration. If you have questions about the status of your application or the evaluation process, please contact .Defcon AI requires its Hiring Platform providers to process your information solely for the purposes described above and in accordance with applicable law. Your information will be retained only for as long as necessary to fulfill these purposes and any applicable legal obligations, after which it will be deleted in accordance with Defcon AI's data retention policies.

For more information about how your data is used, please refer to our Privacy Policy and Applicant Privacy Notice .