Angible is a Taipei-based AI startup founded in 2025, dedicated to accelerating AI adoption in the retail industry through computer vision and edge computing. In our first year, we successfully deployed in-house AI solutions across multiple retail environments spanning Europe, North America, and Southeast Asia, proving our ability to solve real business pain points at scale. The company is now in a critical phase of rapid business expansion and continuous product refinement, focused on building intelligent solutions that improve retail operational efficiency, reduce shrinkage, and enhance the customer experience.

Our team brings deep expertise across AI, edge computing, hardware design, and retail operations, working in a highly collaborative and fast-iterating environment. Driven by the mission to make retail smarter and more efficient, Angible leverages AI and edge computing to help businesses boost profitability and maintain a competitive edge in a rapidly evolving market — making AI a seamless part of everyday operations.

At Angible, we value transparency, clarity of goals, and mutual trust, and we place great importance on each team member's impact and growth. You will have the opportunity to participate directly in building products from 0 to 1 and scaling them beyond, working alongside a team that is both visionary and action-oriented to rapidly validate hypotheses and bring technology to life in real-world scenarios.

Our core product focuses on self-checkout, helping retailers reduce shrinkage risk and improve store efficiency.

The product connects self-checkout terminals with real-time in-store video, and uses AI to assess transaction risk during checkout:
• Detect: Capture customer checkout behavior, product information, and interactions around the self-checkout area.
• Analyze: Run AI inference locally at the edge to identify missed scans, incorrect scans, or other abnormal scenarios quickly, while balancing speed and data privacy.
• Empower: Send real-time alerts to the self-checkout terminal or store staff interface, helping frontline teams take action at the right moment.

Our architecture avoids heavy cloud transmission and expensive cloud computing. It is designed to be deployed quickly and adapted to different store environments.

For Angible, this is more than a loss-prevention product. It is our first core scenario for bringing visual AI into retail operations. Over time, the same technology can expand to more store-level problems and create greater practical value across the retail industry.

We are looking for an AI Product Manager to help shape how Angible turns AI capabilities into real-world retail impact. You will act as an orchestrator across customer problems, business priorities, ML, software, and execution — defining product direction, success metrics, and scalable solutions that create measurable customer value.This role sits at the intersection of product judgment, technical feasibility, and business impact. You will help the team make clear decisions across user value, technical constraints, product quality, business priorities, and delivery speed.

The ideal candidate has strong conviction around business impact and product outcomes. You are comfortable moving through ambiguity, challenging assumptions, validating quickly, and continuously raising the bar for what our products can deliver in real customer environments.

• Define product direction: Clarify customer problems, business goals, and technical constraints to shape product direction, priorities, and measurable success metrics.
• Turn ambiguity into execution: Translate unclear needs into crisp product narratives, decision criteria, priorities, and success metrics so cross-functional teams can align and move fast.
• Drive high-quality decisions: Lead trade-off discussions across speed vs. quality, customization vs. productization, and short-term workarounds vs. long-term solutions, helping the team make clear decisions and follow through.

• 5+ years of product or technical product experience, with close collaboration with engineering, ML / data, or AI teams, and hands-on experience turning technical capabilities into product outcomes.
• Strong technical judgment, with the ability to discuss trade-offs, constraints, risks, and alternatives with Engineering / ML teams and translate them into product decisions.
• Strong business judgment, with the ability to understand customer needs, implementation costs, scalability potential, and business impact, and use them to make prioritization decisions.
• Metrics-driven product thinking, with familiarity using product metrics, model evaluation metrics, and service-level indicators to judge whether the product is truly creating value.
• Practical fluency with AI tools, with the ability to use AI tools / agents to solve day-to-day problems and improve analysis, writing, product discovery, and team collaboration.

• Experience with edge computing, ML model deployment, or production AI systems.
• Experience scaling B2B / enterprise products from early deployments to multiple customers, sites, or large numbers of devices.
• Engineering, data, or AI background, especially when paired with experience driving technical solutions into real product outcomes.
• Deep understanding of AI agents and agentic workflows, with hands-on experience introducing, operating, or improving these workflows within a company or team.
• A high bar for user experience and product quality, with the ability to notice how details affect outcomes in real-world environments.

1. Resume Screening
2. Online Interview
• Deep discussion of one product you have worked on, including business, product, technical, design, and execution aspects.
3. On-site Interview
• Scenario Discussion: Work through a product scenario with different team members, develop a product plan proposal, and discuss it with the hiring manager.
• Culture & Collaboration Interview: Discuss working style, communication style, teamwork, and the role of a PM within the team.
4. Final Interview
• CEO Interview: Discuss values, growth mindset, and expectations for long-term collaboration.
5. Offer & Onboarding

‍The entire process is expected to be completed within two weeks.