Assembled
Software Engineer - AI Agents & Platform
About this role
Assembled is hiring a Software Engineer to build autonomous AI agents that handle customer support at scale. You'll develop foundational features, improve LLM performance through RAG and retrieval techniques, architect LLM infrastructure, and collaborate with customers like Canva and Etsy to solve complex real-world challenges.
What you'll do
- Build new product features like knowledge gap analysis, workflow builders, and advanced information collection with STT/TTS
- Enhance retrieval augmented generation using vector search, document re-ranking, and embedding techniques
- Design LLM infrastructure abstractions and evaluation/logging systems for multi-model integration
- Engage directly with customers to understand needs and improve product usability
- Contribute across coding, user research, planning, and cross-team collaboration
- Foster startup culture focused on quality, positivity, and ownership
What they're looking for
- 5+ years software engineering experience
- LLM and AI agent development
- Retrieval augmented generation (RAG) techniques
- Golang or backend programming
- Information retrieval and vector search
- System design and architecture
- Customer-focused problem solving
- Adaptability in fast-paced environments
Benefits
- Work on cutting-edge AI agent technology
- Collaborate with industry-leading customers (Canva, Etsy, Robinhood)
- Impact small, ambitious team in startup environment
- Access to real-world complex support scenarios with rapid feedback
- Backed by strong funding ($71M from NEA, Emergence Capital, Stripe)
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Assembled
Assembled builds AI-driven customer support software with forecasting, scheduling, and workforce optimization capabilities designed to predict support volume and optimize agent staffing at scale. The company is hiring Software Engineers to develop its design system, frontend product experiences, ML-powered interfaces, and AI-enhanced workflows across engineering and design teams.
- Website
- assembled.com
Likely interview questions
- Walk us through a project where you built LLM-powered features end-to-end. What were the key challenges with model evaluation and how did you measure success?
- How have you approached improving RAG systems in production? Tell us about a time you optimized retrieval quality or ranking.