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Mirage

Software Engineer, Backend

Union Square, New York City$175k–$275kfulltimemidAdded today

About this role

Mirage, an AI-native video editing platform, is seeking a Backend Engineer to design and operate scalable systems powering their generative media product. You'll own critical infrastructure end-to-end, integrate production AI models, and collaborate across product and research teams to turn complex technical challenges into reliable systems.

What you'll do

  • Design and own backend systems end-to-end including services, APIs, data pipelines, and infrastructure
  • Solve complex distributed systems challenges around scaling, concurrency, and performance
  • Deploy, serve, and scale large generative AI models in production environments
  • Instrument systems and run experiments to improve product and system quality
  • Build core platform infrastructure with third-party integrations, storage, security, and internal APIs

What they're looking for

  • Backend system design and architecture
  • Distributed systems and scalability
  • API design and development
  • Data pipeline construction
  • Production ML model deployment and serving
  • Infrastructure and DevOps
  • Python or similar backend language
  • Problem-solving and debugging

Benefits

  • Medical, dental, and vision insurance
  • 401K with employer match
  • Commuter benefits
  • Catered lunch multiple days weekly
  • Dinner stipend for late nights and team offsites
  • Health & wellness perks
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Mirage

Mirage builds an AI-native video platform that leverages generative media and large language models to enable sophisticated video production, editing, and creative workflows. The company is hiring backend engineers, full-stack software engineers, ML engineers, and iOS developers to advance their AI-driven platform and enhance user experiences in web-based and mobile media creation.

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Likely interview questions

  • Describe a backend system you shipped end-to-end in production—what made it complex and how did you handle scaling?
  • Have you integrated or deployed generative AI models in production? What were the key challenges?
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