Full-Time

Knowledge Graph Expert (Knowledge Graph / SQL / LLM)

Turing • Remote • referral150

Company

Turing

Hourly rate

$30 – $70/hr

Location

Remote

Listed

186d ago

Field:: Generalist
Level:: referral150

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About this Role

The Ads Content Understanding team’s mission is to build the foundational engine for interpretable and frictionless understanding of all organic and paid content on our platform. Leverage state-of-the-art applied ML and a robust Knowledge Graph (KG) to extract high-quality, monetization-focused signals from raw content — powering better ads, marketplace performance, and actionable business insights at scale.
We are seeking a Knowledge Graph Expert to help us grow and curate our KG of entities and relationships, bringing it to the next level.

What you'll do

Onboarding of new entities to the Knowledge Graph maintained by the Ads team
Data entry, data labeling for automation of content understanding capabilities
LLM Prompt tuning for content understanding automation
Pipeline Management & Prioritization
Manage Inbound Requests: Act as the primary point of contact for partner teams (Product, Engineering, Analytics) requesting new entities or schema changes.
Strategic Prioritization: Triage the backlog of requests by assessing business impact, urgency, and technical feasibility.
AI-Assisted Curation & Human-in-the-Loop
Oversee Automation: Interact with internal tooling to review entities generated by Large Language Models (LLMs). You will approve high-confidence data, edit near-misses, and reject hallucinations.
Quality Validation: Perform rigorous QA on batches of generated entities to ensure they adhere to the strict ontological standards and factual accuracy required by the KG.
Model Feedback Loops: Participate in ad-hoc labeling exercises (creation of Golden Sets) to measure current model quality and provide training data to fine-tune classifiers and extraction algorithms.
Data Integrity & Stakeholder Management
Manual Curation & Debugging: Investigate bug reports from downstream users or automated anomaly detection systems. You will manually fix data errors, merge duplicate entities, and resolve conflicting relationships.
Feedback & Reporting: Close the loop with partner teams. You will report on the status of their requests, explain why certain modeling decisions were made, and educate stakeholders on how to best query the new data.

What you need

Knowledge Graph Fundamentals: Understanding of graph concepts (Nodes, Edges, Properties)
Taxonomy & Ontology: Experience categorizing data, managing hierarchies, and understanding semantic relationships between entities.
Data Literacy: Proficiency in navigating complex datasets. Experience with SQL, SPARQL, or Cypher is a strong plus.
AI/LLM Familiarity: Understanding of how Generative AI works, common failure modes (hallucinations), and the importance of ground-truth data in training.

Operational & Soft Skills

Analytical Prioritization: Ability to look at a list of 50 tasks and determine the 5 that will drive the most business value.
Attention to Detail: An "eagle eye" for spotting inconsistencies, typos, and logical fallacies in data.
Stakeholder Communication: Ability to translate complex data modeling concepts into clear language for non-technical product managers and business stakeholders.
Tool Proficiency: Comfort learning proprietary internal tools, ticketing systems (e.g., Jira), and spreadsheet manipulation (Excel/Google Sheets).