I build the systems behind combat, progression and difficulty, and the tools that let designers tune them without a rebuild.
I finished a BSc in Computer Science & Engineering in 2024 and I am completing a Master’s in Game Design and Playable Media. Along the way I published a paper on dynamic difficulty as first author, and I am now writing a thesis on whether Riot’s stated balance intentions show up in champion outcomes. Earlier software engineering work, mostly APIs, databases and automated testing, still shapes how I structure and debug what I build.
Available now. Portuguese national with EU work eligibility, open to relocation or remote.
A junior gameplay programmer or technical game designer role on a team that ships. I am most useful where a system has to be built and then tuned: combat, progression, economy and difficulty, together with the editor tooling that makes tuning quick enough to do often.
I would like to work somewhere that treats balance as a measurable question rather than a matter of taste. That is what the thesis is about, and it is the part of the work I would most like to keep doing.
I peaked at Master 235 LP in League of Legends as a Jungle main and played as Varona Esports’ starting Jungler in the LPLOL Second Division, contributing to draft preparation, VOD review, early-game planning and shotcalling.
Playing Jungle means reading the map, planning routes, trading objectives and deciding which win condition to play around.
I also have substantial Teamfight Tactics experience. Outside competitive games, I play broadly across single-player and story-driven games.
Do Riot Games’ stated design intentions align with observed champion outcomes? A champion-level evaluation of balance design across rank, role, and match phase. In progress, not yet submitted.
Final grade 15/20. Class B on the European Grade Comparability Scale.
Selected coursework: Algorithms & Abstract Data Types (16/20) and Parallel & Distributed Computing (17/20)
Taught ICT to 21 classes across four year groups, roughly 525 students. Subjects included digital literacy, productivity and collaboration tools, 3D modelling, media editing, web publishing, copyright and plagiarism. 16 of 21 classes reached full success. I also supported students and staff with hardware, software and digital-tool troubleshooting.
The paper frames adaptive healing through anxiety, empowerment and expected health. The game uses explicit health thresholds to select potion support. Enemy count, damage and behaviour stay unchanged.
A separate Monte Carlo simulation used 5 levels at 1,000 runs each. Modelled failure was 40% with uniform distribution, 56% with random distribution and 8% with adaptive support. In a convenience sample of 38 computer-science students, 83% did not perceive the adaptation. This measures detectability in that sample, not enjoyment or player success.
Read publication (opens in a new tab)I map Riot’s stated balance intentions to champion-specific metrics, then compare patches 25.20 and 25.21 across rank, role and match phase. The current analysis uses 3,362 selected-champion participant observations. It describes alignment with the stated intention, without attributing the observed changes to the patch alone.
Relevant undergraduate study: Statistical Methods, Numerical Analysis, Databases, and Algorithms & Abstract Data Types.
Academic quantitative work included R, alongside Python and SQL.
Read analysisThree-week short course completed with a 92% overall score. Not a formal qualification.
View certificate (opens in a new tab)