SingaporeSenior Product Builder - Consumer AI
3 нед. назад
SingaporeWorldwideSeniorOnsite
aiproduct_managementdesign
Build AI-powered consumer experiences for Asia's leading shopping and rewards platform.
О компании
- Our Journey The Group is Asia-Pacific’s leading shopping, rewards, and payments platform, serving over 20 million active members across 13 markets. In 2025, the Group continued its global growth with its expansion into North America. Driven by the vision to make every day more rewarding, is dedicated to saving members money and time, and delivering delight every day. The platform also enables merchants and brands to engage with their members in a cost-effective manner. Founded in 2014, now powers over US$5.5 billion in annual sales for over 20,000 online and in-store partners, and has rewarded shoppers with more than US$900 million (over S$1 billion) in Cashback to date. Through its innovative offerings, continues to create value for both members and mer
Требования
- Strong design eye with the ability to prototype consumer experiences end-to-end — from rough layout to precise interaction detail
- Can spec requirements in forms that directly unblock engineers: verbal description, production URL reference, component vocabulary, or working prototype
- Work has very few visual issues; holds the line on localisation, edge cases, and interaction precision — not just the happy path
- Has shipped AI-powered features that real users relied on — search, recommendations, generative UI, conversational interfaces, or similar
- Understands where AI adds real value and where it adds noise; can articulate the design principles that distinguish the two
- You direct AI across your daily workflow — copy iteration, UI pattern generation, prototype production, research synthesis, localisation review — and validate outputs against your own judgment. You run materially faster than a conventional workflow without offloading the judgment layer. This is a baseline expectation for this role, not a differentiator.
- Independently strategic — doesn't need the problem handed to them
- At least one team around them is doing better work because of their involvement
- Track record of making the right call in ambiguous, cross-functional situations
- Can point to a consumer metric that moved because of a design decision they owned
- Can show work that others built on — a component, a pattern, a framing — not just work that shipped
Другое
- You take a messy space — shifting user behaviour, an emerging AI capability, a business bet — and sharpen it into a crisp problem statement with a clear success condition.
- You define what great looks like for your surface. You review work, call out the delta between good and great, and hold that standard under deadline pressure.
- You use AI-assisted tools to build working prototypes before specs are written. You don't wait for the design file to be finished before forming a point of view.
- You own the outcome, not just the output. You connect metric movements to specific design decisions and change direction when the data doesn't support the hypothesis.
- When an AI feature ships, you own the quality bar — structured test cases, scoring criteria, and failure mode coverage. AI output is unverified until tested. "It looks right" is not a quality check.
- You actively shape how other builders approach problems — through review, patterns, and shared standards. Leadership is a day-1 expectation at this level.
- At this level, your job isn't just to use AI fluently — it's to help the people around you understand where it creates real leverage in their specific work. Not sharing tools. Demonstrating deliberate practice.
- Token architecture, component docs, and pattern rationale kept in formats that engineers and AI tools can consume. Not just Figma files.
- We have moved past "using AI tools." The team operates in an AI-native workflow: prototypes generated before specs, design systems maintained as machine-readable knowledge layers, eval sets built for AI-assisted surfaces, and no-canvas mode the default for incremental changes.
- AI output on this team is adversarially evaluated, not accepted. Eval sets are shared assets, not personal checklists.
- You will be expected to adopt, model, and evolve these practices — not just follow them.