PbPredictable 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 /bVerification-Focused AI Engineerb who thrives at the intersection of AI capabilities and mathematical precision. /b /ppbr/ppbWe're looking for someone who: /b /ppbr/pullibUnderstands both AI potential and limitations /b—excited about LLMs but equally passionate about making them reliable, traceable, and mathematically sound. /lilibEmbraces functional programming paradigms /b—comfortable with Kotlin, TypeScript, and compositional system design for building deterministic, verifiable AI workflows. /lilibHas curiosity about formal methods /b—interested in SMT solvers, logical reasoning, mathematical validation, or formal verification techniques (experience preferred but not required). /lilibThinks in systems and workflows /b—drawn to event-driven architectures, streaming systems, and building complex verification pipelines rather than just prompt engineering. /lilibValues transparency and explainability /b—motivated by building AI systems where every decision can be traced, verified, and explained with mathematical rigor. /li /ulpbr/ppbIdeal backgrounds include: /b /ppbr/pulliComputer Science with formal methods exposure /liliMathematics/Logic with programming experience /liliSoftware Engineering with AI/verification interest /liliResearch experience in AI safety, verification, or explainable AI /li /ulpbr/ppbThis role involves building verification systems that make AI trustworthy, not just impressive. /b If you're excited about combining the power of large language models with the rigor of formal verification, we want to meet you /ppbr/pullibBuild verification-first AI systems /b alongside senior engineers, focusing on Server-Sent Events architectures, streaming workflows, and real-time verification pipelines using Kotlin and TypeScript. /lilibDevelop and integrate formal verification tools /b—work with SMT solvers, logical reasoning systems, and mathematical validation tools to ensure AI outputs are provably correct and traceable. /lilibDesign streaming verification workflows /b that combine factual verification (web search), logical validation (formal methods), and mathematical checking (computational tools) into coherent, auditable pipelines. /lilibImplement TypeScript client libraries and UI components /b for real-time research steppers, verification progress visualization, and interactive audit trail interfaces with full type safety. /lilibContribute to Docker-based tool ecosystem /b—help maintain and extend the 17+ containerized verification tools, MCP server implementations, and automated deployment systems. /lilibParticipate in verification methodology research /b—explore new approaches to AI fact-checking, logical consistency testing, and mathematical validation while maintaining functional programming principles. /lilibSupport enterprise integration patterns /b—help build authentication systems, multi-tenancy features, and API integrations that allow verification capabilities to be embedded in customer applications. /li /ulpbr/ppbRequired: /b /ppbr/pullibStrong foundation in Computer Science, Mathematics, or Engineering /b—degree preferred but exceptional self-taught candidates with demonstrated systems-building experience welcome. /lilibProficiency in functional programming languages /b—experience with Kotlin, TypeScript, or Scala preferred; comfort with immutable data structures, composable functions, and type-safe architectures. /lilibInterest in mathematical reasoning and formal methods /b—curiosity about logic, proof systems, SMT solvers, or mathematical validation (coursework or personal projects demonstrate this). /lilibSystems thinking mindset /b—experience with event-driven architectures, streaming systems, API design, or containerized applications; understanding that AI is part of larger, reliable systems. /lilibCollaborative engineering skills /b—comfort with Git workflows, code reviews, and building production-quality software rather than just research prototypes. /li /ulpbr/ppbBonus Points: /b /ppbr/pullibFormal methods exposure /b—coursework or projects involving theorem provers, model checking, constraint solving, or mathematical verification tools. /lilibLLM integration experience /b—but focused on reliability, evaluation, and systematic testing rather than just prompt engineering. /lilibFunctional programming enthusiasm /b—personal projects or contributions to FP ecosystems; understanding of monads, type systems, or category theory. /lilibEnterprise software experience /b—authentication systems, multi-tenancy, observability, or building APIs that other developers actually use. /lilibInterest in AI safety/explainability /b—genuine curiosity about making AI systems transparent, auditable, and mathematically sound. /liliHands-on mentorship from experienced AI engineers and researchers. /liliOpportunity to work on real projects with impact in the AI reliability space. /liliFlexible, remote-first working environment. /liliA chance to grow your skills and transition into a full-time role in a fast-growing company. /liliAccess to state-of-the-art AI tools and learning resources. /li /ulpbr/p