AI Agent Software Engineer - Agent Performance Engineering

Assembled builds the infrastructure that underpins exceptional customer support, empowering companies like CashApp, Etsy, and Robinhood to deliver faster, better service at scale. With solutions for workforce management, BPO collaboration, and AI-powered issue resolution, Assembled simplifies the complexities of modern support operations by uniting in-house, outsourced, and AI-powered agents in a single operating system. Backed by $70M in funding from NEA, Emergence Capital, and Stripe, and driven by a team of experts passionate about problem-solving, we’re at the forefront of support operations technology.

Our Agent Intelligence team is focused on maximizing AI agent performance and driving volume automation across voice, chat, and email channels. This team is vital for our vision of creating the ultimate omnichannel AI customer support agent and represents one of the fastest growing areas both inside our company and across the industry.

We're currently one of the largest companies deploying AI agents in production at massive scale, working with enterprise clients like Canva, Etsy, and Ramp. The engineering challenges are significant and largely unsolved: building sophisticated evaluation systems, automating knowledge generation, optimizing AI agent accuracy, and creating the infrastructure needed to continuously improve agent intelligence at scale.

As part of the Agent Performance Engineering team, you'll be working on the core systems that make our AI agents smarter, more accurate, and more capable of handling complex customer interactions. You'll be building the evaluation frameworks, knowledge automation systems, and intelligence optimization tools that directly impact our ability to automate customer support at enterprise scale.

• Build foundational evaluation infrastructure: Develop comprehensive evaluation systems from the ground up, including golden dataset creation, automated benchmarking, and model comparison tools. You'll help create the frameworks that enable us to measure and optimize AI agent performance across all communication channels.

• Automate knowledge generation: Design and implement systems that automatically create synthetic guides, documentation, and metadata to improve agent knowledge bases. You'll work on cutting-edge approaches to knowledge extraction and augmentation.

• Optimize AI agent accuracy: Enhance our retrieval systems, implement advanced prompt optimization techniques, and build tools that continuously improve agent responses through automated evaluation and refinement.

• Develop intelligence infrastructure: Architect systems that enable rapid model upgrades, A/B testing of different AI approaches, and scalable evaluation pipelines that support enterprise deployment.

• Drive volume automation: Focus on the north star goal of maximizing automated resolutions across voice, chat, and email by building the intelligence systems that make it possible.

You might be a good fit if you:

• Have 5+ years of experience in software engineering as an individual contributor

• Have experience with AI evaluation systems, data pipelines, or AI model optimization

• Are passionate about building systems that measure and improve AI performance

• Have worked with retrieval systems, knowledge bases, or information extraction

• Enjoy building tools and infrastructure that enable other engineers and AI systems to perform better

• Are highly ambitious and driven, setting high goals for yourself and others

• Put customers first, focusing on solving real problems that impact support quality

• Enjoy fast-paced environments and can quickly adjust when new insights emerge

• Have a bit of a maverick streak that helps you come up with creative solutions

• Have made a noticeable impact on small teams and have solid experience in startups or smaller companies

• Stay humble and open to feedback, value teamwork, and are always ready to learn and grow

• Python/Golang for evaluation systems and data processing

• LLMs and prompt optimization frameworks

• Vector databases and retrieval systems

• AI evaluation and benchmarking tools

• Data pipeline and automation infrastructure