taxbit

UI Engineer

Seattle, Washington$110k–$170kmidAdded today

About this role

Taxbit, a FinTech innovator automating global tax compliance, is hiring a UI Engineer to build polished, scalable web applications with a focus on front-end architecture and user-facing product experiences. You'll collaborate with designers and product managers to solve complex problems for customers across the digital assets and financial transactions space.

What you'll do

Build elegant and efficient solutions to complex front-end and full-stack problems
Partner with designers and product managers to identify and solve customer problems
Write well-tested, readable, and maintainable code with emphasis on UI quality and reusable patterns
Participate in code reviews to maintain quality and distribute knowledge across the team
Own end-to-end problem-solving across multiple systems and services in collaboration with engineers
Contribute to design systems and component libraries to drive consistency and speed in product development

What they're looking for

React and modern front-end frameworks/libraries
TypeScript, JavaScript, HTML, and CSS
Node.js and/or Python
AWS cloud infrastructure and CI/CD (GitHub)
Cloud storage solutions (SQL, Blob, Document, Search)
DataDog monitoring and observability
Design systems and reusable UI component architecture
AI/ML integration and prompt engineering

Benefits

Competitive salary ($110K–$170K) with equity (RSUs)
Comprehensive benefits package including modern 401(k) with crypto option
Hybrid work: 3 days in-office, 2 days flexible/remote
Discretionary unlimited time off and paid parental leave
Team lunches, snacks, and drinks on Mondays
Fertility benefits and autonomous work flexibility

Likely interview questions

Tell us about a complex, data-heavy React application you built. How did you approach performance optimization and component architecture?
Describe your experience with design systems and reusable UI patterns. How have you ensured consistency across a product?

How have you used AI or machine learning to enhance team productivity or solve a customer problem?
Walk us through your experience setting up and maintaining CI/CD pipelines with GitHub Actions and AWS infrastructure.
Tell us about a time you owned an end-to-end feature that required collaboration across multiple services. How did you coordinate with other engineers?
What's your experience with cloud storage solutions, and how have you chosen the right tool (SQL vs. NoSQL vs. Blob storage) for different use cases?
How do you approach writing maintainable and testable code, especially in a rapidly scaling product environment?

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