Full Stack Engineer, AI systems

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About ActAI There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion.

We believe products will greatly reduce hallucinations. Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things. Role 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.

Focus 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 Experience working with LLMs, RAG systems, or AI-powered applications 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 Outcomes 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 Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring Establish patterns and abstractions for integrating LLMs, memory,