Senior Platform Engineer

Interface AI•Palo Alto, CA

9h•$170,000 - $200,000

About The Position

Banking is being reimagined—and customers expect every interaction to be easy, personal, and instant. We are building a universal banking assistant that millions of U.S. consumers can use to transact across all financial institutions and, over time, autonomously drive their financial goals. Powered by our proprietary BankGPT platform, this assistant is positioned to displace age-old legacy systems within financial institutions and own the end-to-end CX stack, unlocking a $200B opportunity and potentially replacing multiple publicly traded companies. Ultimately, our mission is to drive financial well-being for millions of consumers. With over two-thirds of Americans living paycheck to paycheck, 50% holding less than $500 in savings, and only 17% financially literate, we aim to put financial well-being on autopilot to help solve this problem. About the Role We are building an AI-native platform where agentic systems, real-time services, and distributed infrastructure converge to power intelligent financial products. We are looking for a Senior Platform Engineer who is deeply technical, AI-first by mindset, and excited to build foundational systems through code. This is a senior individual contributor role for someone who: Thinks in distributed systems Codes extensively and confidently Understands AI-native architectures Builds durable, scalable platforms Raises the engineering bar for everyone around them You will write production-grade code daily and own critical components of our core platform

Requirements

7+ years of backend or distributed systems engineering experience.
Strong proficiency in Python and at least one backend language (Node.js or Go).
Proven experience with observability and tracing systems (OpenTelemetry, Prometheus, Grafana).
Experience with CI/CD pipeline design (Jenkins, GitHub Actions, ArgoCD, GitOps workflows).
Hands-on experience with Kubernetes, Helm, Terraform, and declarative infrastructure management.
Deep understanding of distributed systems, concurrency, and performance optimization.
Experience building high-availability, production-scale systems.
Hands-on experience designing APIs and microservices.
Strong debugging skills across complex distributed environments.
Experience deploying systems in cloud-native environments.
Strong debugging skills across complex distributed environments.

Nice To Haves

Experience working with LLMs or AI-driven systems in production.
Familiarity with agentic architectures or multi-agent orchestration.
Experience with streaming systems and event-driven architectures.
Experience building systems under strict latency constraints.
Experience in fintech or regulated environments

Responsibilities

Build AI-First Platform Systems
Design and implement core platforms that power multi-agent orchestration, real-time decisioning, and AI workflows.
Build structured control layers around LLM integrations to ensure reliability and low latency.
Develop abstractions that allow AI systems to operate safely and predictably at scale.
Contribute to evaluation, monitoring, and guardrail systems for AI development lifecycle and agentic architectures.
Engineer High-Performance Distributed Systems
Design and build low-latency infrastructure platforms
Optimize resource utilization and costs
Enable streaming and event-driven platforms to be hosted
Build fault tolerance, resiliency, and failovers.
Enable reducing tail latency and eliminate performance bottlenecks.
Code with Ownership
Write production-quality code in Python and at least one systems/backend language (Node.js or Go preferred).
Review code with rigor and raise engineering standards.
Refactor and simplify, and build abstractions over complex infrastructure systems for maintainability.
Strengthen Platform Foundations
Build reusable service frameworks and platform abstractions.
Improve observability, logging, metrics, and tracing.
Harden authentication, authorization, and secure service-to-service communication.
Drive architectural improvements through RFCs and technical proposals.
Collaborate Across AI & Product Teams
Work closely with engineers and product teams.
Translate product needs into scalable platform capabilities.
Build a security first mindset and frameworks to enable secure SDLC
Support compliance, security, and governance requirements.