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Outtake

Software Engineer, Infastructure

New York CityfulltimemidAdded today

About this role

Staff-level infrastructure generalist role at an AI-powered fraud detection platform. You'll architect and build foundational systems handling billions of content pieces, including databases, search, async processing, and cloud infrastructure, while setting technical direction as the infrastructure function scales.

What you'll do

  • Set technical direction for databases, search/retrieval, data processing, async execution, and cloud architecture
  • Redesign search and retrieval systems to support fast investigation across billions of content pieces
  • Evolve relational database architecture through sharding, partitioning, indexing, and caching strategies
  • Design and build scalable asynchronous systems for scheduling, queues, workflows, and failure recovery
  • Develop shared infrastructure services, APIs, and libraries for product engineers
  • Operate systems in production, use evidence to improve architecture, and lead safe migrations

What they're looking for

  • Distributed systems design and architecture
  • Relational database scaling (sharding, partitioning, indexing, caching)
  • High-throughput asynchronous systems and queueing
  • Large-scale search and retrieval systems
  • Data ingestion, streaming, and batch processing
  • Cloud infrastructure and deployment architecture
  • System migration and production operations
  • Build-versus-buy technical evaluation
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Outtake

Outtake builds an AI-driven platform focused on combating digital impersonation, fraud, and security threats through innovative cloud infrastructure solutions. The company is hiring Software Engineers across full-stack, platform, and security specializations, along with Sales Engineers who bridge technical expertise and customer relationships.

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Likely interview questions

  • Describe your experience designing and operating distributed systems at scale—what was the largest system you've owned and how did you measure success?
  • Walk us through how you've approached a major database migration or scaling challenge in production; what went wrong and how did you handle it?
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