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GigaML

Software Engineer I / II

New York$160k–$250kfulltimemidAdded 1 month ago

About this role

Giga is seeking Software Engineers (I/II) to build backend systems powering AI agents for enterprise customer service. You'll work on data pipelines, integrations, and agent infrastructure alongside experienced engineers at a well-funded startup serving major clients like DoorDash.

What you'll do

  • Develop features for AI assistant platforms including alerts, natural language queries, and resource management
  • Build log visualization and activity stream tools with filtering and analytics capabilities
  • Create dynamic knowledge systems that auto-update from external sources
  • Implement agent memory features for conversation context and interaction lookups
  • Own smaller projects end-to-end while collaborating with senior engineers on larger initiatives
  • Contribute across backend systems including data pipelines and integrations

What they're looking for

  • Backend software engineering
  • Data pipelines and ETL
  • API integrations
  • Database design
  • System architecture
  • Quick learning and iteration
  • Code quality awareness
  • Curiosity and problem-solving

Benefits

  • Competitive base salary, bonus, and equity
  • Daily catered lunch and dinner stipend
  • $500/month wellness and commuter benefits
  • 401(k) plan
  • Medical, dental, and vision coverage
  • Early responsibility and growth opportunities at a funded startup
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GigaML

GigaML builds an AI agent platform that powers enterprise customer service and experience solutions for Fortune 500 companies. The company is hiring forward-deployed engineers, backend software engineers, security engineers, and mobile engineers to develop and deploy AI-powered agents, backend infrastructure, security systems, and mobile SDKs.

View all jobs at GigaML

Likely interview questions

  • Tell us about a project where you built a data pipeline or integration—what challenges did you face and how did you approach them?
  • Describe a time you owned a feature end-to-end. How did you decide what to ship first, and when did you know it was ready?
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