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Learning Process Engineer

passivelogic.com Logo

PassiveLogic

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Location:
Netherlands, Amsterdam

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

Not provided

Job Description:

We’re building the next generation of AI-powered productivity tools for autonomous building management, with seamless interaction between humans and machines at the core. Our team combines deep expertise in AI with elegant product design to create experiences that are natural, intuitive, and delightful. If you’re excited about attracting new users and championing our products while helping us accelerate and strengthen our frameworks, this role is for you. This is a career-defining opportunity to play a crucial role in a hyper-scale AI company that is transforming the future of autonomous systems, energy, and the built environment. We are looking for a Learning Process Engineer to design and implement the technical frameworks through which our Qortex engine learns, adapts, and improves. This role blends machine learning, data engineering, and graph-based knowledge modeling. You will architect the pipelines, feedback loops, and graph-driven logic that enable the system to continuously refine its performance.

Job Responsibility:

  • 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

Requirements:

  • Technical background in computer science, AI/ML, data engineering, or knowledge systems
  • 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
  • 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
  • Background in process engineering, systems design, product operations, or applied AI/ML
  • 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
  • Proven experience: 5+ years in developing software with an ecosystem nature
  • Exceptional communication skills: Ability to craft narratives and messaging that resonate across different engineering and product teams
  • Organized and strategic: Skilled in planning and delivering in an agile manner
  • Collaborative mindset: Enjoy working across teams, contributing to integrated campaigns, and aligning event strategies with overall marketing goals
  • Adaptability: Comfortable in a fast-paced startup environment, eager to learn, iterate, and innovate
  • Problem solving: You own this role. When issues arise, be the empowered force that solves them, rolling-up

Nice to have:

  • 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
  • Experience in knowledge work automation, intelligent assistants, or productivity tools
  • Comfort with data analysis (SQL, Python, or BI tools) to validate process impact
  • Exposure to UX research or behavior-driven design
What we offer:
  • Competitive compensation
  • Generous equity share package
  • Pension plan
  • Paid time off
  • Commute Coverage (NS Business Card or Car allowance)
  • In Office Lunch
  • Fun office-wide activities quarterly
  • Worldwide ski/snowboard pass

Additional Information:

Job Posted:
December 11, 2025

Work Type:
On-site work
Job Link Share:

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