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Open since Mar 2026

Senior Software EngineerPermute · Chicago

Senior Software Engineer

ChicagoOn-siteFull-time

AI Summary

Designs, builds and deploys production AI systems and LLM-powered agent architectures end-to-end, including evaluation, monitoring and reliability infrastructure, in a fast-moving startup.

About this role

Senior Software Engineer – AI Systems
Employment Type: Full-time
Company: Permute (www.permute.ai)

Overview

Permute is seeking a Senior Software Engineer to design, build, and deploy production-grade AI systems in a fast-moving startup environment. This role is for builders who move quickly, think clearly in ambiguous situations, and take ownership of turning ideas into working systems.

We care as much about how you think and build as we do about your background. The ideal candidate is someone who can reason through new problems, prototype rapidly, and ship reliable systems in a fast-moving startup environment.

Responsibilities

  • Design and implement production AI systems and agent architectures end-to-end

  • Build and maintain LLM-powered agents and tool-integration pipelines

  • Develop infrastructure for model evaluation, benchmarking, and monitoring

  • Collaborate with product, research, and engineering teams to deliver AI features

  • Prototype and productionize new approaches in reinforcement learning and agent orchestration

  • Optimize system reliability, performance, and scalability in deployed environments

  • Operate effectively in ambiguous, rapidly evolving startup environments

Required Qualifications

  • Strong background in software engineering and system design

  • Solid foundation in algorithms, statistics, and optimization

  • Experience building AI agents, LLM systems, or ML-driven products

  • 5+ years building in/around AWS infrastructure

Preferred Background

  • Degree in Mathematics, Physics, Computer Science, or a related technical field

  • Experience with:

    • Production Applications end-to-end

    • Agent orchestration frameworks

    • Model evaluation frameworks and benchmarking

    • Safety, monitoring, and reliability for deployed ML systems

Skills

Agent OrchestrationAI AgentsAlgorithmsAWSBenchmarkingLLM SystemsMachine LearningModel EvaluationMonitoringOptimizationProduction SystemsReinforcement LearningStatisticsSystem DesignTool Integration

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