Cory Robbins

AI engineer · Data scientist · Economist · Builder

Résumé / CV

Cory Robbins

AI Engineer / Data Scientist

AI systems engineering · Evaluation engineering · Economics

American based in Hamburg, Germany, currently working with aerospace AI, airframe and fuselage engineering workflows, and applied AI systems at a major aircraft manufacturer.

My work combines AI systems engineering, evaluation engineering, data science, and economics within real production constraints.

Experience

Areas of practice

Current professional focus

Hamburg, Germany

Aerospace AI & airframe engineering workflows

Applied AI systems in aerospace contexts at a major aircraft manufacturer. Public descriptions are intentionally limited to general capabilities.

  • Aerospace AI and applied AI systems
  • Fuselage, airframe, and major aircraft component engineering contexts
  • Collaboration with major aircraft component engineers and technical stakeholders
  • Domain-aware AI and automation
  • Evaluation under real operational constraints

No internal project names, systems, datasets, aircraft details, customer information, or proprietary engineering results are included.

Selected technical work

Ongoing

AI systems, evaluation & automation

Personal and public technical work exploring reliable AI workflows, retrieval, human review, scientific machine learning, and lightweight automation.

  • LLM and retrieval evaluation workflows
  • Human-in-the-loop annotation and feedback
  • Domain NLP and technical-document processing
  • Static publishing and deployment automation

Analytical foundation

Across roles and projects

Economics, data science & systems thinking

An interdisciplinary approach to incentives, uncertainty, measurement, and system behavior—used to frame technical work before choosing a model or tool.

Technical strengths

Selected capabilities

AI systems engineering

Workflow design, retrieval, model integration, guardrails, and production-minded architecture.

Evaluation engineering

Test design, trace review, failure analysis, scorecards, and lightweight EvalOps practices.

Aerospace engineering context

Airframe and fuselage workflows, technical documents, domain context, provenance, and reviewable outputs.

Scientific ML

Experimental design, surrogate modeling, predictive validation, and simulation-oriented workflows.

Human-in-the-loop systems

Annotation, expert review, feedback loops, uncertainty, and clear decision ownership.

Technical communication

Turning specialized problems into understandable structures, interfaces, and written explanations.