Learning Process Engineer Jobs

PassiveLogic

Location:
Netherlands, Amsterdam

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Not provided

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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

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Learning Process Engineer