Senior - AI Engineer

Predictable Machines; La Coruña; A distancia; EUR 50.000 - 70.000; Jornada completa

Descripción de la vacante

A cutting-edge AI company seeks a Verification-Focused AI Engineer to build verification-first AI systems using Kotlin and TypeScript. The ideal candidate has a background in Computer Science or Mathematics, a strong foundation in functional programming, and a keen interest in formal verification methodologies. This role offers a flexible, remote-first working environment with an opportunity to impact AI reliability.

Servicios

Flexible working environment

Access to state-of-the-art AI tools

Growth opportunities in a fast-growing company

Formación

• Strong foundation in Computer Science, Mathematics, or Engineering.
• Proficiency in functional programming languages and experience with Kotlin or TypeScript.
• Interest in mathematical reasoning and formal methods.

Responsabilidades

• Build verification-first AI systems focusing on real-time verification pipelines.
• Develop formal verification tools ensuring AI outputs are correct and traceable.
• Support enterprise integration patterns and build authentication systems.

Conocimientos

Kotlin

TypeScript

Functional Programming

Mathematical Reasoning

Systems Thinking

Educación

Degree in Computer Science/Mathematics/Engineering

Herramientas

SMT Solvers

Docker

Descripción del empleo

Overview

Predictable Machines is building the next generation of verifiable AI systems—combining cutting-edge language models with formal verification, functional programming, and mathematical rigor. We\'re seeking a Verification-Focused AI Engineer who thrives at the intersection of AI capabilities and mathematical precision.

Responsibilities (summarized):

• Build verification-first AI systems alongside senior engineers, focusing on Server-Sent Events architectures, streaming workflows, and real-time verification pipelines using Kotlin and TypeScript.
• Develop and integrate formal verification tools — work with SMT solvers, logical reasoning systems, and mathematical validation tools to ensure AI outputs are provably correct and traceable.
• Design streaming verification workflows that combine factual verification (web search), logical validation (formal methods), and mathematical checking (computational tools) into coherent, auditable pipelines.
• Implement TypeScript client libraries and UI components for real-time research steppers, verification progress visualization, and interactive audit trail interfaces with full type safety.
• Contribute to Docker-based tool ecosystem — help maintain and extend the 17+ containerized verification tools, MCP server implementations, and automated deployment systems.
• Participate in verification methodology research — explore new approaches to AI fact-checking, logical consistency testing, and mathematical validation while maintaining functional programming principles.
• Support enterprise integration patterns — help build authentication systems, multi-tenancy features, and API integrations that allow verification capabilities to be embedded in customer applications.

Ideal backgrounds

• Computer Science with formal methods exposure
• Mathematics / Logic with programming experience
• Software Engineering with AI / verification interest
• Research experience in AI safety, verification, or explainable AI

This role involves building verification systems that make AI trustworthy, not just impressive. If you\'re excited about combining the power of large language models with the rigor of formal verification, we want to meet you.

Qualifications

• Strong foundation in Computer Science, Mathematics, or Engineering — degree preferred but exceptional self-taught candidates with demonstrated systems-building experience welcome.
• Proficiency in functional programming languages — experience with Kotlin, TypeScript, or Scala preferred; comfort with immutable data structures, composable functions, and type-safe architectures.
• Interest in mathematical reasoning and formal methods — curiosity about logic, proof systems, SMT solvers, or mathematical validation (coursework or personal projects demonstrate this).
• Systems thinking mindset — experience with event-driven architectures, streaming systems, API design, or containerized applications; understanding that AI is part of larger, reliable systems.
• Collaborative engineering skills — comfort with Git workflows, code reviews, and building production-quality software rather than just research prototypes.

Bonus Points

• Formal methods exposure — coursework or projects involving theorem provers, model checking, constraint solving, or mathematical verification tools.
• LLM integration experience — but focused on reliability, evaluation, and systematic testing rather than just prompt engineering.
• Functional programming enthusiasm — personal projects or contributions to FP ecosystems; understanding of monads, type systems, or category theory.
• Enterprise software experience — authentication systems, multi-tenancy, observability, or building APIs that other developers actually use.
• Interest in AI safety / explainability — genuine curiosity about making AI systems transparent, auditable, and mathematically sound.
• Hands-on mentorship from experienced AI engineers and researchers.
• Opportunity to work on real projects with impact in the AI reliability space.
• Flexible, remote-first working environment.
• A chance to grow your skills and transition into a full-time role in a fast-growing company.
• Access to state-of-the-art AI tools and learning resources.