ML Engineer, Retrieval & Grounded Generation (Remote)

Openkrill; United States; Teletrabalho; USD 165 000 - 200 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Remote work

Equity package

Health insurance

Unlimited PTO

Flexible schedule

Parental leave

Resumo da oferta

Defcon AI is seeking a seasoned ML Engineer, Retrieval & Grounded Generation to build embeddings, vector storage, and scalable retrieval, ensuring every generated text is tied to cited sources. You will own prompt design, model packaging, and telemetry for a real production system in a secure, government-cloud context.

The role is fully remote with occasional travel to DEFCON AI HQ or customer sites. Candidates should have 5+ years in RAG, strong Python, and US citizenship with active secret

Qualificações

• 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.

Responsabilidades

• 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

Conhecimentos

Python

RAG systems

Embeddings

Vector retrieval

Model deployment

Descrição da oferta de emprego

Defcon AI is seeking a seasoned ML Engineer, Retrieval & Grounded Generation to build embeddings, vector storage, and scalable retrieval, ensuring every generated text is tied to cited sources. You will own prompt design, model packaging, and telemetry for a real production system in a secure, government-cloud context.

The role is fully remote with occasional travel to DEFCON AI HQ or customer sites. Candidates should have 5+ years in RAG, strong Python, and US citizenship with active secret