ML Engineer, Retrieval – Grounded Generation

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop embeddings, vector storage, and retrieval systems at scale utilizing a vast, provenance-tracked evidence base.

• Integrate language models to ensure generated text is linked to cited source records.

• Evaluate citation failures and assess grounding quality.

• Create prompts and output schemas.

• Manage model packaging, versioning, serving, and rollback processes.

• Implement telemetry for retrieval and generation quality, recommendation/version attribution, overrides, abstentions, grounding failures, latency, throughput, and measurement events.

• Provide bounded model assistance for complex narrative extraction, ensuring every output is connected to its source passage.

• Offer recorded rule context for every model-assisted step, including precise rule versions and ordered context.

• Sustain a modular in-boundary serving path, either self-hosted or managed, alongside the primary managed inference service.

• Produce generated explanations, scalable retrieval solutions, trustworthy rollback, and comprehensive measurement telemetry.

⛳️ Requirements

• Over 5 years of experience, including a production or near-production retrieval-augmented (RAG) system that you developed independently.

• Ability to discuss your retrieval design in detail, including the vector store utilized, rationale behind choices, grounding testing methods, practical citation failures, and rollback processes.

• Proficiency in Python, with practical experience in embeddings and vector retrieval at scale.

• Transparency regarding what was delivered in previous projects—whether it was a prototype, proposal, or deployed code—as this distinction is more significant than the title on a resume.

• US Citizenship is required.

• An active US Secret clearance is mandatory to start.

• Preferred: Experience with deploying models in restricted or air-gapped environments.

• Preferred: Operation of self-hosted or open-weight models.

• Preferred: Expertise in fine-tuning, adapters, or custom embeddings.

• Preferred: Familiarity with Federal DevSecOps, RMF, ATO, or DoW cloud environments.

• Preferred: An active Top Secret clearance.

🏝️ Benefits

• A fully remote, results-oriented work environment.

• Competitive salary, bonus, and equity package.

• Comprehensive health insurance for you and your family, fully employer-paid, including medical, dental, and vision coverage.

• Unlimited paid time off (PTO) with manager approval.

• A flexible work environment allowing you to manage your workday.

• 14 weeks of fully-paid parental leave.