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

We are the Platform-as-a-Service Engineering team at EarnIn. We build the foundational "Paved Road" that enables our product teams to ship with velocity and safety. Our stack is built on AWS, EKS, and ArgoCD, but our future is Agentic. We are shifting from manual configuration to AI-augmented orchestration, where the platform anticipates developer needs and self-heals through intelligent automation.

We're seeking a Senior Platform Engineer (I4) to design, build, and operate the agentic systems and core infrastructure that power our platform. You will work across Kubernetes (AWS EKS), GitOps (Argo CD), CI/CD (GitHub Actions), and our developer portal (Cortex), building AI agents and automation that reduce operational toil and increase platform leverage for engineering teams across EarnIn. As a Senior engineer, you independently design and implement solutions to well-scoped but complex problems, re-architect components, adopt new technologies, and improve existing processes with minimal guidance. You'll build agents that handle real slices of incident triage, environment provisioning, config generation, and developer self-service, and you'll partner with Staff engineers and your manager to align your work with the team's broader technical direction.

The 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 (Headquarters) and will require in-office work 2 days a week.

Applicants must be currently authorized to work in the United States. We are able to support H-1B and O-1 employer transfers but are NOT able to provide new visa sponsorship or consider candidates working pursuant to OPT or CPT for this position

WHAT YOU'LL DO

AI-Agent Design and Build: Design, build, and operate AI agents in production, following established governance patterns for model selection, evaluation, and safety. Contribute to infrastructure-as-code practices for agentic systems, ensuring prompts, tools, and evaluation criteria are versioned, reviewed, and tested like any other critical component.
Technical Execution: Implement agentic patterns for cloud infrastructure in your area of ownership, applying and helping refine team best practices for production AI agents. Support and mentor less experienced engineers on agentic patterns, LLM integration, and prompt engineering as opportunities arise.