Senior Fullstack Software Engineer, Gorgias

• Experienced Full-stack Engineer (5+ years preferred): You’ve built and shipped production systems across backend and frontend. Strong mid-level engineers with a clear track record of ownership are welcome to apply
• Hands-on with AI Systems: You’ve worked with LLMs in a real product context, not just prototypes
• Builder Mindset: You’re comfortable owning ambiguous problems and shipping end-to-end solutions
• System Thinker: You understand how to make non-deterministic systems more reliable
• Assertive and Self-Directed: You can operate in an exploratory environment with high ownership
• Strong backend foundations and the ability to quickly pick up new languages. We work in Python and TypeScript, and we hire for problem-solving, not specific stacks
• Production experience integrating LLMs into user-facing products, including multi-turn or agentic systems
• Hands-on experience evaluating prompts and agent behaviour, formally or informally. You’ve had to answer “Is this getting better or worse?” in production
• Experience designing systems that turn structured data (events, user state, transactions) into LLM context
• Experience building and operating APIs and event-driven services
• Comfort with frontend work when needed (React), even if your centre of gravity is backend

Desirable

• You’ll have a chance to:
• 🤖 Build and scale production AI features used by thousands of merchants
• 🧠 Work directly with LLMs, prompts, and agent workflows in a real product environment
• 🏗️ Shape the architecture of AI systems as we continuously evolve and improve our platform
• 🔌 Turn shopper signals, events, behaviours, and order history into context that drives LLM-powered conversations
• Operate in a fast-moving, exploratory team building a new product area
• Develop end-to-end features across backend, APIs, and frontend
• Translate shopper event streams and product data into the context that LLMs can act on
• Own the multi-turn conversational behaviour of our AI Agent: quality, reliability, and iteration in production
• Build and improve evaluation workflows for prompts and agent behaviour
• Define practical ways to measure and improve AI performance in production
• Optimize for cost and latency as models and usage scale
• Evolve the platform as we scale it for higher load and more complex use cases
• Improve data ingestion, context building, and observability
• Partner with product and design to shape AI-driven user experiences

• Recruiter phone screen
• Hiring Manager interview
• System Design interview
• Live Coding interview
• Final interview