Context Compilation – Trustworthy AI-Assisted Systems Engineering

SystemWeaver

. Build a typed model adapter and a rule-based closure engine with provenance tracking .

Posted 10/27/2026internshipGothenburg • SwedenMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building typed model adapters and rule-based closure engines, with a strong focus on LLM evaluation and experimental design. Proficient in Python or C#, with a solid understanding of graph algorithms and typed data models.

Highest-signal resume keywords

Python ProgrammingGraph AlgorithmsLLM EvaluationExperimental DesignTechnical Writing

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Typed Data ModelsPrompt EngineeringStructured OutputStatistical LiteracySystem Building

Soft Skills

Precision in WritingJudgment in System Design

Tools & Technologies

Modern LLM Tooling

Industry Keywords

Engineering PhysicsData ScienceSystems EngineeringSafety-Critical DomainRegulated Domain

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Build a typed model adapter and a rule-based closure engine with provenance tracking
Design a question set and an evaluation protocol with expert-reviewed answers
Compare compiled context against document and graph retrieval under matched context budgets
Measure whether explicit evidence status reduces unsupported claims
Document limitations, failure modes and negative results
Build the prototype against a real industrial model
Design the evaluation and test whether stating what is verified, assumed, stale or unknown changes LLM reasoning reliability
Work under joint supervision with the university
Apply the research within a 30 ECTS, 20-week thesis starting January 2027

Requirements

What you’ll need

Enrolled in an MSc in computer science, data science, engineering physics, systems engineering or similar, with the thesis remaining
Strong programming, and the judgement to build a small system rather than a script
Comfortable with graphs and typed data as a way of thinking: you have written a traversal, a query planner, a constraint or type checker
Working knowledge of modern LLM tooling: prompting, structured output, building an evaluation harness
Enough statistical literacy to design a fair comparison and not over-read a result
Precision in writing: a core contribution of this thesis is a definition
Must be located within commutable distance of Gothenburg, Sweden
Python or C#
Graph algorithms
Typed data models
LLM evaluation
Prompt engineering
Structured output
Experimental design
Applied statistics
Technical writing
Applications in pairs are preferred, or a single exceptional candidate
Meriting, not required: logic programming, Datalog, Prolog, knowledge graphs or formal methods
Meriting, not required: experience in a safety-critical or regulated domain
Meriting, not required: MBSE, SysML or systems engineering exposure

Benefits

Comp & perks

Flexible hours
A real industrial engineering model to work on, not a benchmark dataset
A sharply scoped research question and results worth publishing
Modern workspace at CityGate, Gothenburg’s tallest commercial building