Software Engineer (Gen AI)

EarnIn•Mountain View, CA

16h•Hybrid

About The Position

EarnIn is building the next generation of AI-powered experiences that help millions of people access their pay and improve their financial well-being. We’re looking for a self-driven engineer eager to grow, learn quickly, and expand their impact by building high-quality systems and agent-driven GenAI workflows. This role is on the AI Platform team and is for a builder who is ready to move beyond foundational work and take significant ownership. We are looking for someone who takes initiative, deeply values craft, and is eager to push the boundaries of how engineers collaborate with AI agents. The US base salary range for this full-time position is $181,000 to $222,000, plus equity and benefits. Our salary ranges are determined by role, level, and location. This hybrid position requires in-person work from our Mountain View HQ 2 days a week.

Requirements

3+ years of experience in software engineering
A bachelor’s, or equivalent industry experience
A strong foundation in software engineering and thoughtful system design.
Experience writing code that is reliable, durable, and easily maintained.
Experience in proactively identifying problems, learn quickly, and taking the initiative.
Curiosity and a growth mindset with a focus on and experience stretching beyond what you already know.
Excitement about GenAI, agents, and the future of AI-assisted software development. Hands-on experience is a plus.

Nice To Haves

Experience using AI-assisted development tools (e.g., Copilot, Cursor, LLMs) is a plus

Responsibilities

Build and ship agent-driven chatbot and GenAI workflows that deliver real customer impact.
Own meaningful features end-to-end, from definition through design, all the way to implementation, evaluation, and impact measurement.
Work hands-on with modern AI development tools (e.g., Cursor, Codex, Claude-style assistants).
Collaborate with product, ML, and engineering teams in a fast-moving, ownership-driven culture.
Continuously explore better ways to design, iterate, and improve how we build with AI.