Posted 2mo ago

Senior Software Engineer

@ Permute
Chicago, Illinois, United States
$180k-$280k/yrOnsiteFull Time
Responsibilities:Design systems, Build agents, Develop infrastructure
Requirements Summary:Senior software engineer with AI systems experience, AWS, and 5+ years in relevant work.
Technical Tools Mentioned:AWS
Save
Mark Applied
Hide Job
Report & Hide
Job Description

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