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.
RoleWe 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
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
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, and external tools into scalable product systems
Contribute to a product experience where AI feels proactive, consistent, and dependable over time
Next.js
Python
NodeJs
Pytorch
OpenAI / Anthropic / open-source LLMs
SQl & noSQL
Kubernetes
Docker
The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product
If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.
We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
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