Full Stack Engineer, AI systems

A1

Todos los posibles candidatos deben leer con atención los siguientes detalles de este trabajo antes de presentar una candidatura.

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.

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 xqbhyrx 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, and external tools into scalable product systems
  
• Contribute to a product experience where AI feels proactive, consistent, and dependable over time
  

Tech Stack

• 
  
• Next.js
  
• Python
  
• NodeJs
  
• Pytorch
  
• OpenAI / Anthropic / open-source LLMs
  
• SQL & noSQL
  
• Kubernetes
  
• Docker