Cardless
Software Engineer - Backend
San Francisco$165k–$260kfulltimemidAdded today
About this role
Cardless seeks an experienced backend engineer to design and scale financial systems powering their co-branded credit card platform. You'll own projects across applications, accounts, transactions, fraud, and rewards while collaborating with cross-functional teams in a fast-moving fintech environment.
What you'll do
- Design and build scalable, secure backend systems for financial products across multiple domains
- Lead projects end-to-end from requirements through production deployment and maintenance
- Contribute to architectural decisions and platform evolution as the company grows
- Collaborate with product, design, compliance, and external partners on feature delivery
- Drive improvements in system reliability, observability, performance, and developer experience
- Mentor peers and provide technical guidance across the engineering organization
What they're looking for
- Backend systems design and implementation at scale (5+ years experience)
- Distributed systems, APIs, databases, and infrastructure fundamentals
- Proficiency in Java, Python, or similar modern backend languages
- Deep expertise in databases, scaling systems, or distributed systems
- End-to-end project delivery and cross-functional collaboration
- Public-facing API design and maintenance
- Financial systems or data integrity knowledge (nice to have)
- ML pipelines or feature store exposure (nice to have)
Benefits
- Competitive salary ($165K–$260K) with startup equity
- 100% health, vision, and dental primary coverage; 75% dependent coverage
- Catered lunches, $250/month commuter benefit, and Apple equipment
- Parental leave and flexible PTO with minimum 15 days annually
- 401k plan and team building events
- 5-day in-office in San Francisco's Jackson Square
Likely interview questions
- Tell us about a complex backend system you designed at scale—what challenges did you face and how did you solve them?
- Describe your experience with distributed systems. How do you approach ensuring data consistency and reliability?
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