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Bjak

Product Engineer - AI Investing

  • Confirmed live in the last 24 hours
  • No salary listed
  • Mid level
  • Full-time
  • Remote · United States
  • Added 1 month ago

About this role

BJAK seeks a Product Engineer to own AI Investing product areas, bridging user needs with engineering execution. You'll define strategy, collaborate cross-functionally, and ship financial products that simplify complex workflows for users across Southeast Asia and the US market.

What you'll do

  • Own product areas in insurance, payments, savings, investing, or travel within the AI Investing platform
  • Define user problems, product scope, success metrics, roadmap and launch strategies
  • Collaborate with engineering, design, data, operations, and compliance teams to deliver features
  • Transform complex financial products into intuitive, user-friendly customer experiences
  • Use data, user feedback, and production insights to iterate and improve product decisions
  • Launch features, monitor performance metrics, and drive post-launch optimization

What they're looking for

  • Product management or product ownership experience
  • Fintech, insurance, payments, or consumer app domain knowledge
  • Data analysis and metrics-driven decision making
  • User research and feedback synthesis
  • Cross-functional collaboration and stakeholder management
  • Technical fluency to work effectively with engineers
  • Clear written and verbal communication
  • Rapid prioritization and execution under uncertainty
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Bjak

Bjak is a Southeast Asian fintech super app offering insurance, payments, savings, wallets, and investment products through a unified platform. The company is hiring full stack engineers, backend engineers, iOS developers, and Android engineers to build scalable features and reliable systems across mobile and web products.

Industry
Banking & Finance
Website
bjak.com
View all jobs at Bjak

Likely interview questions

  • Tell us about a financial or complex product you shipped—what was the user problem you solved, and how did you measure success?
  • Describe a time you had to make a product decision without complete data. How did you approach it?
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