Software Engineer developing AI-driven solutions for a remote SaaS company. Leading product features end-to-end with a focus on backend and frontend systems.
Responsibilities
Own and lead product features end-to-end: research, design, implement, test, and ship
Lead and shape engineering architecture - set standards for code quality, testing, and development practices across the stack
Mentor and guide other engineers in adopting AI-native workflows, establishing best practices and patterns
Talk directly to customers and translate vague requirements into polished user experiences
Design scalable backend systems (Python, FastAPI, PostgreSQL, GCP)
Write clean, maintainable, and testable code with minimal oversight
Drive architectural decisions and engineering processes that maximize AI-assisted development velocity
Ship fast and learn fast - high urgency environment
Requirements
2–6 years of professional backend leaning full-stack experience at a product-first company or FAANG-level environment (Senior SDE ownership expected)
Comfortable building features across the entire stack—from schema design and backend APIs to pixel-perfect, accessible frontend
Care deeply about product, design, and user experience—not just code
Have owned something meaningful end-to-end (a product module, infrastructure refactor, major feature launch)
Can set standards for engineering quality and architectural decisions in an AI-native environment
Resourceful and thrive in ambiguous environments—you figure things out independently
Have strong product intuition and can push back on requirements when needed to improve outcomes
AI coding experience is your most important and necessary skill - you have deep understanding of modern coding tools, specifically Claude Code, and are proficient with its various modes (Interactive, Plan, Headless, Agent Teams, Delegate)
You're "AI-pilled" when it comes to problem-solving - you tackle any new problem, skill, or architectural challenge by leveraging AI tools and an AI-driven workflow. This is your default approach, not an experiment
Strong systems thinking and architectural awareness - you direct AI to build while you architect, review, and verify. You know when AI output is correct and when it needs correction
Can demonstrate recent AI-native work - you've shipped meaningful projects in the last 90 days where AI coding tools were your primary development environment.
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