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Gallatin AI

Backend Engineer

El Segundo, CA$80k–$210kfulltimemidAdded 1 month ago

About this role

Gallatin seeks a Backend Engineer to build scalable, AI-driven logistics systems supporting national security missions. You'll develop robust APIs, data pipelines, and backend services while collaborating across product and AI teams in El Segundo, CA.

What you'll do

  • Design and develop scalable backend services and APIs for logistics platform
  • Build data ingestion pipelines handling structured, unstructured, real-time, and batch sources
  • Maintain system architecture with focus on performance, reliability, and security
  • Implement comprehensive testing strategies including unit, integration, and performance tests
  • Participate in code reviews and contribute to best practices documentation
  • Support team growth through mentoring and knowledge sharing

What they're looking for

  • Backend languages: Python, Node.js, TypeScript, or Java
  • API design and implementation (REST, GraphQL)
  • Relational and NoSQL databases (PostgreSQL, MongoDB, DynamoDB, Redis)
  • Cloud platforms (AWS, GCP, Azure)
  • Docker and Kubernetes containerization
  • Infrastructure as code (Terraform, CloudFormation)
  • Software testing methodologies
  • Complex system debugging and problem-solving
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Gallatin AI

Gallatin AI builds AI-powered logistics optimization systems for national security and defense operations, focusing on feasibility validation, allocation algorithms, routing networks, and supply chain decision-making. The company is hiring AI Engineers, Backend Engineers, and Infrastructure Engineers to design scalable, secure systems that ensure military logistics plans are compliant, auditable, and operationally robust.

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Likely interview questions

  • Walk us through how you'd design a scalable API architecture for handling high-volume data ingestion from multiple structured and unstructured sources.
  • Describe your experience with relational and NoSQL databases—how do you decide which to use for different parts of a system, and how have you optimized query performance?
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