About The Position

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. This is a deeply technical, hands-on role. Work directly with engineers on system design, evaluation, and trade-offs-defining requirements, but shaping how the system works for global users. You work at the intersection of user needs, model capability, and system constraints, and are responsible for turning AI potential into real, reliable behavior in a real-world application.

Requirements

  • Strong grounding in computer science fundamentals, including algorithms, data structures, and system design.
  • Solid understanding of ML fundamentals and how modern AI systems behave in production.
  • Comfort reading, reviewing, and discussing technical design documents.
  • Hands-on exposure to AI-powered products, including LLM-based systems.
  • Experience working with model evaluation, prompt or pipeline iteration, and feedback loops.
  • Strong intuition for model limitations, hallucinations, bias, and drift.
  • Significant experience owning complex, technical products end-to-end.
  • Proven ability to work closely with senior engineers and ML teams.
  • Strong judgment and decision-making ability in ambiguous, fast-moving environments.
  • Ability to balance ambition with technical and operational reality.

Nice To Haves

  • Experience shipping AI-heavy consumer products.
  • Background as an engineer or highly technical product manager.
  • Experience defining evaluation metrics for ML systems.
  • Strong intuition for AI UX patterns and failure handling.
  • Prior experience in zero-to-one product environments.

Responsibilities

  • Research and define end-to-end AI system requirements from capability to behavior to user impact
  • Translate model capabilities, data constraints, and evaluation results into clear product and system decisions
  • Make hard trade-offs across quality, latency, cost, reliability, and UX
  • Work closely with ML, backend, and mobile engineers on system design, evaluation, and iteration
  • Define and evolve evaluation frameworks across offline metrics, online experiments, and human feedback
  • Drive execution with clear specs, strong judgment, and disciplined prioritization
  • Ensure systems ship quickly, safely, and reliably, with strong feedback loops
  • Own product quality end-to-end - correctness, predictability, and user trust
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