Product Lead, AI Email App

About the role

The company builds proactive, AI-native applications for everyday tasks such as email, notes, tasks and calendar, aiming to bring intelligence to conversations, errands and workflows with minimal prompting. As Product Lead for the AI Email App, you sit at the intersection of user needs, model capability and engineering constraints, working directly with ML and engineering teams to define how the system behaves, how it is measured and how it keeps improving. It is a deeply technical, hands-on role.

Key responsibilities

• Define end-to-end requirements for AI capabilities, from model and system behavior to user experience.
• Translate model capabilities, evaluation results and technical constraints into product and system decisions.
• Make trade-offs across quality, latency, cost, reliability, safety and UX.
• Work with ML, backend and client engineers on system design, evaluation and iteration.
• Define and evolve evaluation frameworks across offline metrics, online experiments and human feedback, and set quality bars and feedback loops.
• Drive execution through clear specifications, prioritization and strong technical judgment.
• Identify failure modes so AI systems behave predictably and recover gracefully, and own product quality end-to-end.

Required profile

• Significant experience owning complex, technical products end-to-end.
• Hands-on experience with AI-powered products, especially LLM-based systems.
• Ability to turn ambiguous problems into clear requirements, measurable outcomes and executable plans.
• Strong judgment and the ability to decide when the right answer is not obvious.
• Highly technical, curious, and comfortable challenging engineers and researchers constructively.
• Comfortable with significant ownership in a fast-moving, zero-to-one environment.

Required skills

• Strong computer science fundamentals and system design skills.
• Solid understanding of modern ML and how AI systems behave in production.
• Experience with model evaluation, experimentation, prompting or AI system iteration.
• Nice to have: shipping AI-heavy consumer products, defining evaluation metrics for ML systems, and an engineering background.

The interview process involves up to four virtual or onsite interviews, evaluated by the technical team, with a prompt decision.