About the role
About the Role
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations
Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things
As Product Engineer, AI Email App, you work at the intersection of user needs, model capability, and engineering constraints. You will work directly with ML and engineering teams to define how the system should behave, how we measure it, and how we continuously improve it.
This is a deeply technical, hands-on role. You are expected to understand the system and challenge technical decisions.
What You'll Own
Define end-to-end requirements for AI capabilities, from model behavior to system behavior to user experience.
Translate model capabilities, evaluation results, and technical constraints into clear product and system decisions.
Make trade-offs across quality, latency, cost, reliability, safety, and UX.
Work closely 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.
Establish clear quality bars and feedback loops for AI-powered experiences.
Drive execution through clear specifications, prioritization, and strong technical judgment.
Identify failure modes and ensure AI systems behave predictably and recover gracefully.
Own product quality end-to-end, correctness, reliability, usefulness, and user trust.
What We're Looking For
Technical
Strong computer science fundamentals and system design skills.
Solid understanding of modern ML and how AI systems behave in production.
Comfortable reading technical designs, understanding architectures, and engaging deeply with engineers.
Strong intuition for model limitations, hallucinations, evaluation, and failure modes.
Product & AI
Significant experience owning complex, technical products end-to-end.
Hands-on experience with AI-powered products, particularly LLM-based systems.
Experience with model evaluation, experimentation, prompting, or AI system iteration.
Ability to turn ambiguous problems into clear requirements, measurable outcomes, and executable plans.
Strong judgment and the ability to make decisions when the right answer is not obvious.
Mindset
Highly technical, curious, and comfortable going deep.
Strong bias toward shipping, experimentation, and learning from real-world usage.
Able to challenge engineers and researchers constructively while maintaining strong collaboration.
Comfortable operating with significant ownership in a fast-moving, zero-to-one environment.
Nice to have
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.
An engineering background is a strong plus, but what matters most is the ability to reason deeply about both AI systems and product outcomes.
Outcomes
Product strategy clearly aligns AI capabilities with user needs and company priorities.
AI features deliver real value, are understandable, predictable, and trusted by users.
Decisions balance quality, speed, cost, and reliability effectively under uncertainty.
Roadmaps and priorities are clear, with fast iteration based on real user feedback.
Teams are aligned, focused, and able to execute on AI product goals with minimal friction.
How We Work
We are a small, high-talent-density, hands-on team. Engineers and product leaders have broad ownership and are expected to exercise strong judgment and execute independently.
We make decisions quickly, work closely together, and balance speed with engineering fundamentals. We care less about process and more about building something exceptional.
Interview process
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.
Originally posted on Himalayas