PassiveLogic is building the next generation of AI-powered productivity tools for autonomous building management, with seamless interaction between humans and machines at the core. The company is looking for a Learning Process Engineer to design and implement the technical frameworks through which their Qortex engine learns, adapts, and improves, blending machine learning, data engineering, and graph-based knowledge modeling to architect pipelines, feedback loops, and graph-driven logic that enable the system to continuously refine its performance.
Requirements
- Experienced with graph databases (Neo4j, TigerGraph, Weaviate, Neptune), Python/C++, graph query languages (Cypher, Gremlin, GraphQL, SPARQL), graph ML/embeddings, and building ETL pipelines, event-driven systems, and real-time feedback loops.
- Understanding of feedback-driven model improvement, reinforcement learning, or adaptive systems.
- Strong systems thinking: ability to model complex workflows and simplify them into actionable processes.
- Familiarity with human-in-the-loop learning, adaptive systems, or feedback-driven workflows.
- Experience with LLM fine-tuning, RAG (retrieval-augmented generation), or hybrid search (vector + graph).
- Knowledge of MLOps workflows and deploying AI systems in production.
- Familiarity with ontologies, semantic reasoning, or graph-based recommendation systems.
Responsibilities
- Architect feedback pipelines: Build and maintain data ingestion and labeling processes that transform user interactions into structured learning signals.
- Design graph-based knowledge structures: Model, update, and optimize workflows in a graph database (e.g., Neo4j, ArangoDB, Weaviate, or similar).
- Implement adaptive logic: Use graph queries and embeddings to inform recommendations, predictions, and workflow adaptation.
- Integrate human-in-the-loop learning: Deploy mechanisms that incorporate user corrections and contextual feedback into graph representations and model updates.
- Collaborate with ML and software engineers: Define retraining strategies, model evaluation criteria, and experiment frameworks that leverage graph-based data.
- Automate performance monitoring: Develop dashboards and metrics for tracking how graph-driven learning impacts system accuracy, adoption, and efficiency.
Other
- Practitioners who are passionate about understanding people, committed to lifelong learning, and driven by the love of what they do.
- Experience working cross-functionally with engineers, designers, and product managers.
- Analytical mindset: ability to define success metrics, run experiments, and interpret results.
- Excellent communication skills and a collaborative, problem-solving approach.
- Proven experience: 5+ years in developing software with an ecosystem nature.
