ML Engineer: Build Trusted AI Pipelines & Knowledge Graph

Factagora; Republic (MO); Hybride; USD 90 000 - 130 000; Plein temps

Résumé du poste

Factagora is seeking an engineer to turn verifiable AI into reality by designing and improving ML pipelines behind its API endpoints, including Fact-checking, Evidence Finding, Causality Graph, and Timeseries extraction. Beyond training models, you'll help build our TKG system and evaluate reliability, tracking how facts change over time.

You’ll work on RAG retrieval and production deployment, applying ML/NLP to global sources.

Qualifications

• 2+ years ML/NLP experience or equivalent research and development.
• Production ML development with Python (PyTorch, HuggingFace).
• Experience applying an LLM or RAG system to a real service.
• NER, relation extraction, text classification in NLP.
• Systematic model evaluation and experiment management.
• Ability to read papers and implement ideas quickly.

Responsabilités

• Designing and improving ML pipelines for core API endpoints such as Fact-checking, Evidence Finding, and Deep Research.
• Developing unstructured text structuring models for FactBlock generation (NER, relation extraction, claim detection).
• Building the Temporal Knowledge Graph and tracking how facts change over time.
• Optimizing the RAG pipeline and improving retrieval accuracy and evidence reliability.
• Designing and improving an LLM-based multi-agent architecture (Agent Debate).
• Researching structured reasoning features like causality graphs and timeseries extraction.
• Building model evaluation frameworks and production deployment pipelines.
• Monitoring and automating knowledge graph quality from global sources.

Connaissances

2+ years ML/NLP

Python

PyTorch

HuggingFace

LLM or RAG systems

NER

Relation extraction

Claim detection

Model evaluation

Experiment management

Reading & applying papers

English communication

Outils

HuggingFace tooling

PyTorch tooling

Description du poste

Factagora is seeking an engineer to turn verifiable AI into reality by designing and improving ML pipelines behind its API endpoints, including Fact-checking, Evidence Finding, Causality Graph, and Timeseries extraction. Beyond training models, you'll help build our TKG system and evaluate reliability, tracking how facts change over time.

You’ll work on RAG retrieval and production deployment, applying ML/NLP to global sources.