Full-Time

Learning Process Engineer in the U.S

Posted on 10/31/2025

Passive Logic

Passive Logic

No salary listed

Murray, UT, USA

In Person

Onsite role in Holladay, UT; no remote option stated; must work in-person.

Category
AI & Machine Learning (2)
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Requirements
  • Technical background in computer science, artificial intelligence/machine learning, 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.
  • 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.
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.
Desired Qualifications
  • 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.

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INACTIVE