Senior Product Builder Search
Job Location:
Monthly Salary:
Posted:
Application Deadline:
Vacancies:
Job Summary
ShopBack serves over 20 million shoppers across 13 markets with cashback deals and discovery. Were now building the layer on top: AI-native shopping experiences that personalise guide and delight at scale.
This role is for someone who closes the gap between product thinking and search depth and ships AI-powered search experiences that people genuinely use. Not a Product Manager with search opinions. Not an engineer with product instincts. But a builder who operates at the intersection and holds the bar across both.
Youll own search quality end to end relevance ranking query understanding and the eval loop behind them in a team that is building AI-first from the ground up.
- Frame the problem not just the solution. 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.
Set the quality bar. You define what great looks like for search. You review work call out the delta between good and great and hold that standard under deadline pressure.
Prototype to think. You use AI-assisted tools to build working prototypes including ranking and routing changes tested against real queries before specs are written. You dont wait for a finished spec before forming a point of view.
Lead the product strategy for your domain. You own the outcome not just the output. You connect metric movements to specific relevance and ranking decisions make the trade-offs explicit (relevance vs. latency relevance vs. paid placement) and change direction when the data doesnt support the hypothesis.
Build the operations and eval layer for search. When an AI feature ships you own the quality bar eval sets built from real queries LLM-as-judge scoring validated against human labels and failure mode coverage. AI output is unverified until tested. It looks right is not a quality check.
Lift the team around you. You actively shape how other builders approach problems through review patterns and shared standards. Leadership is a day-1 expectation at this level.
Model AI-native practice for the team. At this level your job isnt just to use AI fluently its to help the people around you understand where it creates real leverage in their specific work. Not sharing tools. Demonstrating deliberate practice.
Maintain search knowledge as a shared layer. Ranking principles eval sets and decision rationale kept in formats that engineers and AI tools can consume. Not just slide decks.
Product and technical depth not one or the other. Strong product judgment with enough technical depth to prototype and reason about search end-to-end from query understanding to retrieval and ranking. Can spec requirements in forms that directly unblock engineers: verbal description production URL reference query set scoring rubric or working prototype. Work holds the line beyond the happy path edge cases long-tail queries and localisation.
Genuine fluency with consumer AI products and the Search domain. Has shipped AI-powered features that real users relied on search recommendations ranking conversational interfaces or similar. Hands-on with eval pipelines (ideally LLM-as-judge) and familiar with hybrid retrieval and intent classification. Understands where AI adds real value and where it adds noise; can articulate the principles that distinguish the two.
AI as a primary co-worker not an occasional shortcut. You direct AI across your daily workflow query-log analysis judge-prompt writing eval harnesses prototype production research synthesis 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.
Seniority that scales beyond yourself. Independently strategic doesnt 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.
The building/selling test. Can point to a consumer metric that moved because of a search decision they owned relevance ranking or query understanding. Can show work that others built on an eval set a pattern a framing not just work that shipped.
We have moved past using AI tools. The team operates in an AI-native workflow: prototypes generated before specs knowledge maintained as machine-readable layers and eval sets built for AI-assisted surfaces.
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.
- Agency - We take ownership and act rather than waiting for permission. When somethings blocking progress we find a way through it and follow through until its done.
- Judgement- We aim for high-impact decisions not just easy wins and we put the bigger picture ahead of individual interests. That means moving quickly and confidently while staying thoughtful about when a call really matters.
- Learning Velocity - We pick up new skills fast and let go of old habits just as quickly when something better comes along. We benchmark ourselves against the best and keep raising our own bar.
- Tenacity - We stay in it when things get hard keeping a level head under pressure. We debate openly before deciding then commit fully and support each other along the way.
- Career growth opportunities to take on greater challenges that help you realise your ambitions.
- Be part of a winning team on a journey to global scale.
- Competitive compensation based on performance.
- Candid open and collaborative culture where feedback is valued.
Required Experience:
Senior IC
About Company
Latest discount coupons, promo codes & vouchers for your online shopping. Plus FREE cashback rewards from ShopBack. Best deals & promotions!
