Staff Adaptive Trust‑Weighted Retrieval Architect

corpora-jobs-1778796293285-db9d41c6 - Frontier Development

Algorithm DeveloperStaff1 position

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Why This Role is Different

Frontier Development Role

Architect the next‑generation retrieval engine that learns to trust the right vectors, dynamically adjusts to query context, and thwarts membership inference and poisoning attacks—all while preserving semantic recall.

The Frontier Element

You will build a retrieval system that treats trust as a first‑class feature, learning adaptive weighting from live feedback and integrating graph consistency checks—an unprecedented fusion of information retrieval, cryptography, and online learning.

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Project Context

Research Area

Dynamic Trust‑Weighted Retrieval

From: Retrieval Unreliability and Knowledge Base Corruption

Why This Role is Critical

The core of the defense is a retrieval engine that blends similarity, provenance, and adaptive trust scores. This role will design the scoring model, the adaptive alpha schedule, and the integration with the hybrid dense‑sparse‑graph retrieval stack.

What You Will Build

A modular trust‑scoring engine that ingests provenance metadata, historical query success, and peer‑reviewed annotations; an adaptive ranking algorithm that balances similarity and trust; and a real‑time evaluation framework that measures the impact on hallucination rates.

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Key Responsibilities

Design the composite ranking function α·similarity + (1‑α)·trust, and implement an online learning mechanism that adapts α based on query confidence and past success metrics.
Integrate the trust‑scoring engine with the hybrid dense‑sparse retrieval pipeline, ensuring minimal latency overhead while preserving top‑k recall.
Develop a graph‑consistency checker that validates multi‑hop relationships and flags suspicious vector clusters.
Create a continuous evaluation suite that measures hallucination reduction, membership inference resistance, and semantic utility across multiple benchmark datasets.
Collaborate with the cryptographic provenance team to ingest signed metadata into the trust calculation pipeline.

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Required Skills & Experience

Technical Must-Haves

Information Retrieval (dense & sparse embeddings, BM25, FAISS)

Expert

Core to building the hybrid retrieval engine.

Graph databases (Neo4j, Dgraph, TigerGraph)

Advanced

Needed for multi‑hop consistency checks.

Online learning & bandit algorithms (UCB, Thompson Sampling)

Proficient

Used for adaptive alpha tuning.

Python/Scala/Java backend development

Advanced

For implementing retrieval services and scoring modules.

Statistical evaluation of retrieval quality (NDCG, MAP, hallucination metrics)

Expert

Critical for measuring defense effectiveness.

Experience Requirements

5+ years building production‑grade retrieval systems or search engines.
Demonstrated experience with hybrid dense‑sparse retrieval and graph‑based reasoning.

Education

PhD or Master’s in Computer Science, Information Retrieval, or Machine Learning with a focus on retrieval or graph analytics.

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Preferred Skills

Experience with reinforcement learning for ranking.
Knowledge of differential privacy techniques for membership inference mitigation.

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You Will Thrive Here If...

Comfortable experimenting with novel ranking signals and publishing internal whitepapers.
Strong bias toward building end‑to‑end pipelines that can be iterated quickly.

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Impact & Growth

12-Month Impact

Achieve a 30% reduction in hallucination rates and a 50% drop in membership inference success within 12 months, while maintaining sub‑200 ms retrieval latency on a 1 billion‑token corpus.

Growth Opportunity

Scale the trust‑weighted retrieval engine to support multi‑tenant, regulated domains (healthcare, finance) and evolve it into a reusable platform for other AI services.

Ready to Push the Boundaries?

If this sounds like the challenge you have been looking for, we want to hear from you. We value what you can build over where you have been.