ML Engineer

An engineer who turns 'verifiable AI' into reality

You'll design and improve the ML pipelines behind Factagora's API endpoints, including Fact-checking, Evidence Finding, Causality Graph, and Time series extraction. Beyond training models, you'll help build our TKG (Temporal Knowledge Graph) system, which extracts knowledge from global news and research sources, evaluates reliability, and tracks how facts change over time.

• Designing and improving ML pipelines for core API endpoints such as Fact-checking, Evidence Finding, and Deep Research
• Developing unstructured text structuring models for Fact Block generation (
  NER, Relation Extraction, Claim Detection
  , and more)
• Building the TKG (Temporal Knowledge Graph) and improving how it tracks facts changing over time
• Optimizing the RAG pipeline and improving retrieval accuracy and evidence reliability
• Designing and improving our LLM-based multi-agent architecture (Agent Debate)
• Researching and implementing structured reasoning features such as Causality Graph and Time series extraction
• Building model evaluation frameworks and operating production deployment pipelines
• Monitoring and automating knowledge graph quality from global news and research sources

• 2+ years of ML/NLP work experience, or equivalent research and development experience
• Experience developing and deploying ML models in production with Python (PyTorch, Hugging Face, and similar)
• Experience applying an LLM or RAG system to a real service
• Modeling experience with NLP tasks such as text classification, named entity recognition, and relation extraction
• A systematic approach to model evaluation and experiment management
• Ability to read papers and quickly implement and apply them in practice

• Research or development experience with Knowledge Graphs or Graph Neural Networks
• Research experience in fact-checking, claim verification, or misinformation detection
• Experience implementing a multi-agent LLM system or a self-correcting pipeline
• Experience building large-scale news or document processing and information extraction pipelines
• Experience setting up MLOps environments and serving or monitoring models
• Experience operating a service for a global user base, or comfortable communicating in English
• Strong understanding of and interest in AI or deep-tech products

• You're fundamentally curious about why AI-generated information gets things wrong
• You're comfortable turning ideas from papers into prototypes quickly and applying them in production
• You judge success by whether users can genuinely trust the result
  , not just model performance numbers
• You're comfortable breaking uncertain problems into small experiments and validating them quickly
• You genuinely connect with Factagora's mission of building a trustworthy AI and information ecosystem

Application 1st Interview 2nd Interview Final Offer

If you run into any issues during the application process or have questions about this role, reach out to
and we'll get back to you as soon as possible.