Bjak
Product Engineer - AI Stockbroking
About this role
BJAK seeks a Product Engineer to own product areas within its AI Stockbroking platform, focusing on financial services like investing, payments, and savings. You'll define product direction, collaborate across engineering and design, and launch features while balancing user experience with compliance and business needs.
What you'll do
- Own and define product areas across insurance, payments, savings, investing, or travel financial services
- Identify user problems, establish success metrics, and create roadmaps and launch plans
- Collaborate with engineering, design, data, operations, compliance and business stakeholders
- Transform complex financial products into intuitive, usable customer experiences
- Monitor post-launch performance and drive iterative improvements using data and feedback
- Make trade-off decisions between speed, UX, business impact, compliance and operational complexity
What they're looking for
- Product management or product ownership experience
- Complex workflow simplification and product sense
- Cross-functional collaboration with engineers and designers
- Data analysis, metrics, experimentation and user research
- Strategic writing, prioritization and stakeholder management
- Fintech, insurance, payments or consumer app domain knowledge
- Execution-oriented mindset with high ownership
- Decision-making under uncertainty
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
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.
- Website
- bjak.com
Likely interview questions
- Tell us about a complex financial or technical product you simplified for users—how did you approach it?
- Describe a time you shipped a feature quickly with incomplete data. What did you learn after launch?