Full Stack Engineer, AI systems

Responsibilities

• ● There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.
• ● Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over 90% reduced time.
• ● We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users.
• ● Build end-to-end product features across frontend, backend, and AI integrations
• ● Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps.
• ● Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions
• ● Design real-time AI interactions with streaming, partial results, and tight latency constraints
• ● Improve system reliability, observability, and fallback mechanisms
• ● Collaborate closely with ML, backend, and product teams to ship features end-to-end
• ● Continuously iterate based on real usage and failure modes
• ● IDEAL EXPERIENCES
• ● Strong experience in full stack engineering (frontend + backend)
• ● Solid understanding of system design and API architecture
• ● Ability to handle ambiguity and make pragmatic engineering decisions
• ● Strong ownership - able to take features from idea to production
• ● Comfort working in fast-moving environments with evolving requirements
• ● Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows
• ● Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions
• ● Reduce latency and improve responsiveness of AI interactions while maintaining output quality
• ● Build robust fallback and recovery mechanisms for LLM and tool failures in production environments