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Block

Legal Systems Engineer

San Francisco, CA, United States of AmericamidAdded yesterday

About this role

Block seeks a Legal Systems Engineer to architect and evolve the technology infrastructure powering their Legal team. You'll design scalable systems integrating legal platforms, build AI-driven automation workflows, and establish best practices for safely deploying LLMs across complex legal operations.

What you'll do

  • Design and maintain scalable legal systems architecture, ensuring integration and adaptability
  • Identify and execute high-impact automation and AI opportunities to streamline legal workflows
  • Lead design and deployment of AI solutions using Claude, Harvey, and internal LLM platforms
  • Build and oversee integrations across DocuSign CLM, Brightflag, Tonkean, AODocs, and other legal tools
  • Develop APIs, automations, and data pipelines connecting systems and reducing manual effort
  • Provide technical leadership for eDiscovery and Information Governance workflows

What they're looking for

  • Software engineering and systems architecture (7-10+ years)
  • Python, JavaScript, or similar programming languages
  • SaaS platform integration and API design
  • AI tools and LLM implementation (Claude, Copilot, Codex)
  • ELM/eBilling platforms (Brightflag preferred)
  • eDiscovery and EDRM lifecycle management
  • Data platforms (Databricks, Snowflake)
  • Legal workflow understanding (contracts, billing, discovery)
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Block

Block builds financial infrastructure and lending platforms powering multiple brands including Cash App and Afterpay, along with bitcoin mining hardware through its Proto division. The company is hiring Software Engineers, Backend Infrastructure specialists, Product & Test Engineers, and Legal Systems Engineers to scale payments, lending systems, mining ASICs, and legal technology operations.

Website
block.xyz
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Likely interview questions

  • Describe your experience integrating multiple SaaS legal platforms—what challenges did you encounter and how did you overcome them?
  • Tell us about a time you translated experimental AI prototypes into production-ready solutions. What guardrails or patterns did you establish?
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