LLM Application Engineer

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.

• Strong software engineering fundamentals with experience building AI-powered applications
• Hands-on experience with LLMs, generative AI, or agent-based systems
• Experience designing prompts, workflows, evaluations, or AI behaviour
• Ability to write clean, production-quality code
• Comfortable working across abstraction layers (model → system → product)
• Strong problem-solving skills in ambiguous, fast-moving environments
• Bias toward shipping, iteration, and continuous improvement

• Build and ship LLM-powered applications and AI agent workflows
• Design systems for reasoning, planning, memory, tool use, and multi-step execution
• Build reliable orchestration pipelines that convert probabilistic model outputs into predictable, observable, and safe actions
• Integrate LLMs with APIs, databases, search, internal services, and external tools
• Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behaviour
• Build evaluation frameworks and datasets to measure AI quality, reliability, and regressions
• Debug AI systems across the entire stack—from model behaviour and prompts to orchestration, backend services, and product UX
• Optimise AI systems for quality, latency, and cost
• Work closely with product and engineering teams to convert ambiguous product problems into working AI solutions
• Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement