Original listing text, shown exactly as published by the company.
What You'll be Doing
- 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
What You Will NeedTechnical foundation
- 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.
AI & ML experience
- 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.
Product leadership
- 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 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.
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.
