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EarnIn

On-demand wage access via app

Senior Platform Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-timeMountain View, CA, USA

About the Job

Requirements

  • A bachelor's or master's degree in Computer Science, Engineering, or a related field is required.
  • At least 4 years of experience in cloud infrastructure, working on large-scale, high-availability, customer-facing distributed systems is required.
  • Experience mentoring junior engineers and leading well-scoped platform initiatives within a team is required.
  • Hands-on experience building and operating AI-driven systems in production, including agentic workflows that perform operational tasks, is required; candidates should be able to discuss specific agent designs or automation they have built.
  • Experience reducing operational toil through agentic AI in at least one meaningful workflow, such as LLM-powered incident diagnosis, intelligent CI/CD with test selection, or self-service assistants, is required.
  • Strong working knowledge of AWS, including EKS, Lambda, and Bedrock, and experience with containerized and serverless architectures are required.
  • Expertise in Kubernetes at scale and the ability to implement complex, resilient solutions with guidance from senior or staff engineers are required.
  • Strong knowledge of infrastructure-as-code tools such as Terraform and Ansible, and experience applying traditional infrastructure-as-code and agentic automation, are required.
  • Strong command of Datadog and observability practices, using metrics to drive decisions, is required.
  • Adherence to security, privacy, and compliance best practices, with awareness of production AI governance such as model safety, prompt injection prevention, and data isolation, is required.
  • Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, CrewAI, or custom agentic architectures, and production prompt engineering, is required.
  • Strong coding ability in Python and/or Go, and experience treating infrastructure and agentic systems as software, are required.
  • Ability to collaborate across engineering, product, and security teams on cross-functional work is required.

Responsibilities

  • Design, build, and operate production AI agents, 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 other critical components.
  • Implement agentic patterns for cloud infrastructure within the 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.
  • Operate and improve high-availability distributed systems on AWS, identifying and resolving performance, scalability, and stability issues within the role’s domain.
  • Use AI-driven observability and anomaly detection to identify problems early.
  • Evolve infrastructure-as-code and automation standards for owned services, incorporating agentic pattern recognition and automated remediation where valuable.
  • Contribute to the evolution of the Cortex developer control plane 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.
  • Document agentic architecture, best practices, and operational procedures for the systems built.
  • Participate in on-call rotations and use post-mortems to improve system reliability and agentic automation.

Desired Qualifications

  • Experience building AI-driven alerting or root-cause analysis systems is a plus.
  • Experience with service mesh technologies such as Linkerd or Istio and traffic management at scale is a plus.
  • Proficiency with GitOps tools such as Argo CD or Flux CD and CI/CD orchestration tools such as GitHub Actions or Argo Workflows is a plus.
  • Experience with MLOps or LLMOps concepts, including model versioning, evaluation frameworks, and production monitoring for AI systems, is a plus.
  • Familiarity with security frameworks relevant to AI systems, including guardrails, audit logging, and data governance for LLMs, is a plus.

About the Company

EarnIn

On-demand wage access via app

EarnIn provides wage access through a mobile app, allowing users to cash out a portion of their earned wages before payday without traditional fees or interest. Users choose how much to withdraw, with the deduction taken from their next paycheck; Balance Shield automates savings by topping up if balance falls too low; Tip Jar encourages saving and community contributions. The model relies on voluntary payments from users, enabling pay-what-you-want contributions rather than mandatory fees. The company emphasizes a community-driven approach to financial fairness and security with 256-bit encryption.

Company size
501-1,000
Company stage
Debt Financing
Total funding
$565.1M
Headquarters
Mountain View, CA
Founded
2012

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