Sebastian Landl
Service Area Lead Data & AI, DACH
Salzburg, Salzburg, Austria
Summary
🤩
Rockstar🎓
Top SchoolSebastian Landl is a Data & AI service lead and hands-on engineer with six years of experience building production-grade software and machine learning solutions across energy, access control, and large-scale web platforms. Grounded in mathematics and compiler theory, he bridges low-level technical rigor—evident from contributions to a self-hosting RISC-V educational project—with practical product delivery in agile teams. At Gofore he combines strategic leadership for the DACH region with direct implementation of custom RAG systems and secure, domain-specific data workflows. His work on BLE-based localization and iOS integrations shows a knack for solving messy real-world sensing and scale problems, while earlier full-stack roles honed his performance-oriented engineering practices. Outside of work he pursues 3D modeling and animation and trains in Jiu-Jitsu, reflecting a creative and disciplined approach to problem solving.
6 years of coding experience
4 years of employment as a software developer
Diplom-Ingenieur, Computer Science, Diplom-Ingenieur, Computer Science at Paris Lodron Universität Salzburg
English, German
Github Skills (16)
c1710
compiler10
symbolic-execution10
compiler-compiler10
c1110
emulation10
assembler9
debug9
assembly9
risc-v9
virtual-machine9
debugging9
algorithms8
data-structures8
algorithm8
Programming languages (5)
Github contributions (5)
cksystemsteaching/selfie
Nov 2019 - Apr 2021
An educational software system of a tiny self-compiling C compiler, a tiny self-executing RISC-V emulator, and a tiny self-hosting RISC-V hypervisor.
Role in this project:
Backend Developer
Backend DeveloperContributions:2 releases, 6 reviews, 25 commits in 1 year 5 months
Contributions summary:Sebastian significantly contributed to the `selfie` project by adding crucial features related to symbolic execution, particularly focusing on the handling of procedure calls and returns. They introduced a call stack to the context, essential for tracking procedure execution, and implemented recursion handling. Their work involved modifying the core interpreter logic, including the `constrain_beq` function and the `monster` function, and adjusting exception handling to manage new execution scenarios effectively. These changes enhanced the system's ability to analyze and reason about the behavior of the tiny compiler and emulator.
emulatorsymbolic-execution-enginecompilercomputer-sciencecompiling
ChrisEdel/vehicle-classification
Aug 2021 - Jan 2022
The aim of this project is to use the LiDAR sensor of the new Apple devices in combination with the camera sensors in order to classify cars (based on the make and model) using deep learning. This repository contains the corresponding code consisting of the iOS App, a preprocessing and augmentation script, and our custom neural network.
Contributions:1 review, 28 commits, 2 PRs in 5 months
aimsensorscameracarssensor
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