Senior AI Platform Engineer

As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for those with the unique needs of 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 are excited to continue bringing world-class talent onboard to help shape the next chapter of our growth journey.

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 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 for well-scoped but complex problems, re-architecting components, adopting new technologies, and improving 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.

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.
• Platform Operations: Operate and improve high-availability distributed systems on AWS, identifying and resolving performance, scalability, and stability issues within your domain. Use AI-driven observability and anomaly detection to catch problems early. Evolve infrastructure-as-code and automation standards for the services you own, incorporating agentic pattern recognition and automated remediation where it adds value.
• Developer Platform: Contribute to the evolution of our developer control plane (Cortex) as an AI-augmented self-service platform, building features that let engineers scaffold services, debug deployments, and resolve issues through natural language. Help encode platform standards and security policies into AI-powered golden paths.
• Reliability and Documentation: Document agentic architecture, best practices, and operational procedures for the systems you build. Participate in on-call rotations, and use post-mortems as a feedback loop to improve system reliability and your team's agentic automation.

WHAT WE’RE LOOKING FOR

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• 4+ years in cloud infrastructure, working on large-scale, high-availability, customer-facing distributed systems.
• Experience mentoring junior engineers and leading well-scoped platform initiatives within your team.
• Hands-on experience building and operating AI-driven systems in production, including agentic workflows that perform operational tasks. You should be able to speak to specific agent designs or automation you've built.
• Experience reducing operational toil through agentic AI (e.g., LLM-powered incident diagnosis, intelligent CI/CD with test selection, self-service assistants) on at least one meaningful workflow.
• Strong working knowledge of AWS (EKS, Lambda, Bedrock, etc.) and experience with containerized and serverless architectures.
• Solid expertise in Kubernetes at scale and ability to implement complex, resilient solutions with guidance from senior/staff engineers.
• Strong knowledge of infrastructure-as-code tools (Terraform, Ansible) and experience applying both traditional IaC and agentic automation.
• Strong command of Datadog and observability practices, using metrics to drive decisions. Experience building AI-driven alerting or root-cause analysis systems is a plus.
• Strong adherence to security, privacy, and compliance best practices, with awareness of governance considerations for production AI systems (model safety, prompt injection prevention, data isolation).
• Experience with LLM orchestration frameworks (LangChain, LlamaIndex, CrewAI, or custom agentic architectures) and production prompt engineering.
• Strong coding ability in Python and/or Go, with experience treating infrastructure and agentic systems as software.
• Ability to collaborate effectively across engineering, product, and security teams on cross-functional work.
• Experience with service mesh (Linkerd, Istio) and traffic management at scale is a plus.
• Proficiency with GitOps (Argo CD, Flux CD) and CI/CD orchestration (GitHub Actions, Argo Workflows) is a plus.
• Experience with MLOps or LLMOps concepts (model versioning, evaluation frameworks, production monitoring for AI systems) is a plus.
• Familiarity with security frameworks relevant to AI systems (guardrails, audit logging, data governance for LLMs) is a plus.

#LI-Hybrid

At EarnIn, we believe that the best way to build a financial system that works for everyday people is by hiring a team that represents our diverse community. Our team is diverse not only in background and experience but also in perspective. We celebrate our diversity and strive to create a culture of belonging. EarnIn does not unlawfully discriminate based on race, color, religion, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, legally protected medical condition, family care status, military or veteran status, marital status, registered domestic partner status, sexual orientation, genetic information, or any other basis protected by local, state, or federal laws. EarnIn is an E-Verify participant.

EarnIn does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or HR team.

Voluntary Self-Identification

For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.

As set forth in EarnIn’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection. As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measure the effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categories is as follows:

A "disabled veteran" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service-connected disability.

A "recently separated veteran" means any veteran during the three-year period beginning on the date of such veteran's discharge or release from active duty in the U.S. military, ground, naval, or air service.

An "active duty wartime or campaign badge veteran" means a veteran who served on active duty in the U.S. military, ground, naval or air service during a war, or in a campaign or expedition for which a campaign badge has been authorized under the laws administered by the Department of Defense.

An "Armed forces service medal veteran" means a veteran who, while serving on active duty in the U.S. military, ground, naval or air service, participated in a United States military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.

A Note on AI in the Hiring Process:

EarnIn may use Artificial Intelligence (AI)-powered tools during the hiring process, including tools that (i) generate summaries or notes for interviewers, (ii) review resumes, (iii) assist with scheduling interviews, (iv) help identify themes across candidate responses and profiles for a given role, and (v) assist with populating the interview scorecard.

Final hiring decisions are made by human interviewers and hiring managers.

If you have any questions about the use of AI in our hiring process or if you would like to opt out, please follow the "Learn More" link below. Opting out will not negatively affect your candidacy or evaluation.