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Backend Engineer - Secure Systems & Infrastructure

Point Wild

Salary not specified
Aug 14, 2025
Remote, US
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Point Wild is looking to solve the problem of customers' identities and personal information being at risk in a digital world by creating a comprehensive portfolio of industry-leading cybersecurity solutions.

Requirements

  • Deep fluency in modern backend development (e.g., TypeScript with Node.js/NestJS, or Go/Python if appropriate)
  • Strong understanding of distributed systems, API ergonomics, and architectural tradeoffs
  • Familiarity with authentication protocols, cryptographic primitives, and secure data handling practices
  • Experience with event-driven architecture, stream processing, or telemetry ingestion pipelines
  • Proficiency in designing and evolving data models and working with document or time-series databases
  • Prior work in infosec, vulnerability management, or cryptographic systems (optional)
  • Hands-on experience with TypeScript frameworks like NestJS or tRPC (optional)

Responsibilities

  • Build and maintain high-performance backend services to ingest telemetry, orchestrate analysis, and support remediation workflows
  • Design internal APIs and gRPC/REST interfaces for coordination between distributed agents, front-end systems, and external integrations
  • Model and persist system state, cryptographic metadata, and remediation context using scalable storage and indexing strategies
  • Collaborate closely with AI engineers on inference infrastructure and LLM integration patterns
  • Define service-level objectives (SLOs), instrument observability metrics, and ensure fault tolerance under edge conditions
  • Contribute to CI/CD pipelines, security reviews, and deployment automation for fast, safe iteration
  • Engage in design discussions across the stack to help shape foundational architecture decisions

Other

  • 5+ years of backend engineering experience
  • Ability to thrive in high-trust, ambiguity-rich environments with strong ownership and product sensibility
  • Bias toward clean abstractions, thoughtful naming, and maintainability under iteration pressure