How will you make an impact?  

• Build agentic systems and full-stack prototypes end-to-end, shipping a first version in days and learning from real use
• Track the frontier (new models, agent patterns, protocols like MCP) and turn the promising ones into working prototypes rather than slideware
• Design experiments that show whether a concept holds up, measured in quality, latency, cost, and user impact
• Work directly with internal users, product managers, and customers to validate concepts early and iterate quickly 
• Turn validated prototypes into reference implementations and write-ups that product teams can build on, then hand off cleanly and move to the next bet
• Give product and platform teams concrete feedback on where AI capabilities shine and where they fall short
• Move between initiatives as priorities shift. What you learn on one bet compounds into the next

Have you got what it takes?

• Have 4+ years building full-stack software, including things you started from nothing. We weigh what you've shipped more than years on a CV 
• Have shipped LLM-powered systems to production:features real users depended on, not demos or notebooks 
• Understand AI agents below the framework level: message arrays, tool calling, context management, prompt caching. You could build an agent loop from scratch and explain why it works
• Use AI-assisted development (Claude Code, Codex, Cursor, or similar) as your default way of working, and it makes you measurably faster, not just busier
• Are full-stackin practice: comfortable taking a prototype from API to demo UI on your own (our stack centers on TypeScript/Node.js and React) 
• Do your best work when the ground is still moving. New territory energises you more than a settled plan
• Think in user problems first, solutions second

You will have an advantage if you also have:

• A strong eye for UX/UI: you can make a prototype feel like a product, and you notice when one lacks that eye
• Experience with voice and real-time AI: streaming, speech-to-text/text-to-speech, voice-to-voice models
• Open-source work on agent tooling (MCP servers, harnesses, evals)
• Experience designing evaluations for AI systems
• Background in conversational AI or customer-experience products

How will you make an impact?  

• Build agentic systems and full-stack prototypes end-to-end, shipping a first version in days and learning from real use
• Track the frontier (new models, agent patterns, protocols like MCP) and turn the promising ones into working prototypes rather than slideware
• Design experiments that show whether a concept holds up, measured in quality, latency, cost, and user impact
• Work directly with internal users, product managers, and customers to validate concepts early and iterate quickly 
• Turn validated prototypes into reference implementations and write-ups that product teams can build on, then hand off cleanly and move to the next bet
• Give product and platform teams concrete feedback on where AI capabilities shine and where they fall short
• Move between initiatives as priorities shift. What you learn on one bet compounds into the next

Have you got what it takes?

• Have 4+ years building full-stack software, including things you started from nothing. We weigh what you've shipped more than years on a CV 
• Have shipped LLM-powered systems to production:features real users depended on, not demos or notebooks 
• Understand AI agents below the framework level: message arrays, tool calling, context management, prompt caching. You could build an agent loop from scratch and explain why it works
• Use AI-assisted development (Claude Code, Codex, Cursor, or similar) as your default way of working, and it makes you measurably faster, not just busier
• Are full-stackin practice: comfortable taking a prototype from API to demo UI on your own (our stack centers on TypeScript/Node.js and React) 
• Do your best work when the ground is still moving. New territory energises you more than a settled plan
• Think in user problems first, solutions second

You will have an advantage if you also have:

• A strong eye for UX/UI: you can make a prototype feel like a product, and you notice when one lacks that eye
• Experience with voice and real-time AI: streaming, speech-to-text/text-to-speech, voice-to-voice models
• Open-source work on agent tooling (MCP servers, harnesses, evals)
• Experience designing evaluations for AI systems
• Background in conversational AI or customer-experience products
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