Master thesis in software engineering: adaptive architecture for human-ai annotation systems

Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match.

Thesis Scope & Problem Statement

Volvo Autonomous Solutions develops systems for automated hauling operations in quarries and open pit mines using Volvo trucks. Validating these systems requires high-quality ground-truth datasets. Our team owns a camera-based annotation tool that produces bounding boxes used as a reference for validating LiDAR- and radar-based moving-object perception, with camera perception entering the stack. Errors in these reference annotations can compromise the validity of downstream evaluations.

A previous Master's thesis evaluated different architectures for combining AI-generated annotations with human verification. The results highlighted trade-offs between annotation correctness, process performance, maintainability, and human effort: no single static configuration performed best across these dimensions. Participants also reported verifying less actively when AI suggestions appeared plausible, highlighting a potential risk of automation bias.

This thesis project is intended for two Master's students working together.

The central focus of this Master's thesis is to design, implement, and evaluate a confidence-aware adaptive verification architecture within the existing annotation tool. Instead of applying the same review process every detection or scene, the system will use confidence signals to select an appropriate depth of human verification. This is a software engineering thesis focused on how to structure, integrate, test, and evaluate that adaptive behavior, with particular attention to maintainability, traceability, fallback behavior, and effective human oversight. Existing AI models provide the application context; developing new perception models is not the primary objective.

Research Objectives & Tasks

The work may include:

• Design and integrate a modular architecture with explicit interfaces for confidence provider verification policies, decision logging, and annotator feedback.
• Implement confidence-provider components that expose calibrated or normalized signals, such as model confidence, model agreement, source model, and object class frequency, through a common interface.
• Implement and compare interchangeable threshold-based and learned decision policies for routing cases to light review, full review, or a second review. Automatic acceptance will be considered only where permitted by safety policy, with low-confidence, out-of-distribution, and safety-critical cases routed to human review.
• Integrate attention cues into the annotation interface and record the signals, policy version, selected review mode, and human outcome for each routing decision.
• Develop automated tests for routing decisions, component interfaces, and fallback behavior.
• Benchmark the adaptive architecture against existing manual, sequential, and parallel workflows using annotation correctness, recall on safety-critical classes, error catch rates, task completion time, routing overhead, and human intervention.
• Evaluate maintainability and testability through change scenarios, such as adding a confidence provider or replacing a decision policy.
• Conduct a counterbalanced user study to investigate verification behavior, cognitive workload, trust calibration, and susceptibility to automation bias.

Depending on feasibility and measurable added value, the work may also explore out-of-distribution detection or vision-language-model-derived scene complexity as additional confidence signals. The co-evaluation will use fixed policies during controlled experiments; online adaptation is an optional extension.

Who Are We?

You will join the Verification & Validation (V&V) Virtual Driver team at V.A.S., focused on ensuring the integrity of the software stack for autonomous decision-making and control. The team brings together developers with varied backgrounds and seniority, all centered around robust V&V practices. The environment is friendly and helpful, with both in-office development and in-vehicle testing.

Who Are You?

You are a final-year Master's student in Software Engineering, Computer Science, or a related field, with strong interest in software architecture, software quality, and building maintainable systems. You are comfortable programming in Python and interested in extending and evaluating an existing software platform in an industrial setting.

You should have a foundation in modular software design, interface design, automated testing, and version control, together with an interest in empirical software engineering. You should be motivated to compare design alternatives and connect architectural decisions with measurable effects on correctness, performance, maintainability, and testability.

Familiarity with AI-assisted systems, machine learning, computer vision, or human-computer interaction is advantageous, but prior specialization in machine learning or object detection is not required. An interest in learning enough about model confidence and human verification to make sound software design decisions is important.

Why Join Us?

This thesis offers an opportunity to apply software engineering methods to a concrete industrial challenge designing an adaptive human-AI workflow that is maintainable, testable, and auditable. You will build on existing annotation platform and prior thesis results, taking the work from architectural design through implementation and empirical evaluation.

You will:

• Gain hands-on experience in architectural design, component integration, automated testing, and evaluation of an industrial software system.
• Investigate how confidence signals can guide human attention while preserving traceability and human oversight.
• Measure architectural trade-offs across annotation correctness, performance, maintainability, testability, and human effort.
• Collaborate with engineers working on the validation of autonomous transport solutions.

You will gain practical experience in designing and evaluating human-AI systems in a safety-critical context. We look forward to receiving your application letter, academic transcript, and CV in English.

Ready for the next move?

Are you excited to bring your skills and disruptive ideas to the table? We can’t wait to hear from you. Apply today!

Feel free to contact me if you have further questions

Ian MacIsaac, Head of Environment & VD Testing, ian.macisaac@volvo.com

Preferred start date: January 2027 Application deadline: 15 November 2026

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Who we are and what we believe in

We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.

Applying to this job offers you the opportunity to join Volvo Group. Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. We are passionate about what we do, and we thrive on teamwork. We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment.

Part of Volvo Group, Volvo Autonomous Solutions accelerates the development, commercialization and sales of autonomous transport solutions, focusing on defined segments for the on- and off-road space. The combination of strong tech expertise and skilled customer solutions creates innovative transport offers never seen before. We are constantly pushing our own skills and ability to drive change in a traditional industry to meet a growing customer demand. We are now looking for innovative, committed individuals to join us in our endeavor to create customer solutions that enhance safety, flexibility and productivity.