ABOUT EARNIN

As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for people living paycheck to paycheck. Our community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks. We’re fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, Ribbit Capital, and a very healthy core business with a tremendous runway. We’re growing fast and excited to bring on world-class talent to help shape the next chapter of our growth journey.

EarnIn is becoming an AI-native company, embedding AI agents into how we build, analyze, and operate across every function. Teams use AI Agents to move from ideas to execution faster. By letting agents handle repetitive tasks, our people can focus on judgment, creativity, and hard problems. We pair this experimentation with the strict review and security guardrails our regulated financial environment requires, always keeping a focus on privacy and the people who rely on our products.

As a company, we are becoming AI-native, and we encourage you to use AI tools throughout much of your interview process. If you use AI during the live interview, just say so; a quick, natural mention of how it helped and why you are using it is all we need. What we're evaluating is your judgment, reasoning, and ability to own and defend your work, not whether AI was involved. Some rounds, particularly those focused on your career history and past experiences, will not allow the use of AI, and we'll let you know of those in advance. When in doubt, ask your recruiter or interviewer directly.

POSITION SUMMARY

EarnIn is making AI-native engineering a core capability — not an initiative, but how we design, build, and ship. We're not hiring a software engineer who dabbles in AI. We're hiring an AI builder who also writes great software — someone who looks at every step of how we design, build, test, and ship, and asks: why isn't an agent doing this? This is not a side project. The agents and harnesses you build here will run in production, making real decisions for real people — people who depend on EarnIn to access their pay when it matters most. If that bar excites you rather than intimidates you, keep reading.

You'll work at the intersection of platform engineering, developer experience, and applied AI — partnering with architects, domain leads,, and product engineers to build the tools, patterns, and guardrails that make AI adoption fast, safe, and durable. The Mountain View base salary range for this full-time position is $228,000 - $279,000, plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a hybrid position in Mountain View that requires in-office work 2 days a week.

WHAT YOU'LL DO

Agents that take real actions. You'll design how agents think — prompts, reasoning chains, tool calls, and the full architecture beneath them. From MCP servers and agent scaffolding to context harnesses and a Skills Marketplace, you're building the layer every squad at EarnIn builds on. Not a proof of concept. Production infrastructure.

A PDLC that doesn't look like 2022. The product development lifecycle is overdue for a rethink. You'll challenge each stage — from scoping and design through to review, testing, deployment, and monitoring — and replace manual friction with agentic workflows wherever it makes sense. Evaluation pipelines, automated PR hygiene, deployment gating, generation-to-merge metrics: you'll build the scaffolding that makes governed AI fast, not slow.

Evals that actually mean something. You'll own the evaluation infrastructure — building the pipelines, benchmarks, and quality gates that tell us whether our AI is working, degrading, or ready to ship. That means designing eval harnesses for AI-assisted workflows, setting generation-to-merge and review latency baselines, and making model quality visible and trustworthy across teams. If you've built evals that caught real problems before they hit production, you'll fit right in.