Raúl Martínez
Co-Founder, AI Research at Predictable Machines
Greater Cádiz Metropolitan Area Spain
Summary
🤩
RockstarRaúl Martínez is a seasoned software engineer and AI researcher with 13 years of experience, now co-founding Predictable Machines to advance applied LLM and formal verification work. He combines deep functional-programming expertise—evident from substantive contributions to high-profile open-source projects like Arrow and Cats—with practical backend engineering across JVM ecosystems. Previously he led functional initiatives at Xebia and served as CTO for Xebia Functional, modernizing legacy systems into responsive, data-oriented architectures. His background spans enterprise systems at Boeing to graph-backed social platforms, giving him a rare blend of rigorous engineering and product-focused delivery. Beyond leadership, Raúl’s hands-on work on parsers, typeclass instances and data structures shows a strong bent toward correctness and testable abstractions, reflecting his interest in formal methods. Based in Cádiz, he brings a collaborative, kind approach to technically challenging problems.
13 years of coding experience
23 years of employment as a software developer
Spanish, English
Stackoverflow
684reputation
10kreached
12answers
0questions
Github Skills (20)
kotlin10
parser10
typeclass10
arrow-keys10
data-structure10
scala10
cat10
data-structures10
functional-programming10
arrows10
arrow-js10
testing9
computer-engineering9
graalvm-native-image6
generics6
Programming languages (18)
Github contributions (5)
arrow-kt/arrow
Mar 2017 - Dec 2021
Λrrow - The perfect companion for your Kotlin journey - Inspired by functional, data-oriented and concurrent programming
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 releases, 259 reviews, 824 commits in 4 years 9 months
Contributions summary:Raúl primarily contributed to the codebase by addressing Detekt-related issues and implementing several features in the Kategory library, focusing on data structures and functional programming principles. Their contributions included modifying and updating core data types like `ListKW`, and also included providing syntax helper methods for common use cases. The user demonstrates a strong understanding of functional programming by working on `Either`, `Option`, `Validated`, and data structure implementation.
kotlin-dslfunctional-programmingtypeclassesarrowfor-comprehension
scala-exercises/scala-exercises
Sep 2014 - Mar 2017
The easy way to learn Scala.
Role in this project:Back-end Developer
Back-end DeveloperContributions:125 commits, 144 PRs, 171 pushes in 2 years 6 months
Contributions summary:Raúl's contributions center on enhancing the exercise parser and evaluator within the Scala Exercises project. These changes primarily involve modifying and extending code within the `Extractors.scala` and `ExercisecodeExtractor.scala` files. The focus is on adding new functionality for handling different types of operations within the exercises.
functional-programmingscalajsscalapostgresqlshapeless
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