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Mirage

Software Engineer, Backend

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

About this role

Join Mirage as a Backend Software Engineer to build scalable systems powering an AI-native video editing platform. You'll own end-to-end backend services, APIs, and ML pipelines while working closely with product and AI teams in our NYC headquarters.

What you'll do

  • Design and own backend systems end-to-end including services, APIs, data pipelines, and infrastructure
  • Solve complex challenges in distributed systems, scaling, concurrency, and performance
  • Deploy, serve, and scale generative AI models in production environments
  • Instrument systems and iterate in production to improve quality and user outcomes
  • Design core platform infrastructure with third-party integrations, storage, and security
  • Collaborate with product, design, and AI teams to translate ambitious ideas into reliable systems

What they're looking for

  • Backend system design and architecture
  • Distributed systems and scalability
  • API design and development
  • Data pipeline engineering
  • Generative AI model integration and deployment
  • Production infrastructure and DevOps
  • Problem-solving and rapid iteration
  • Cross-functional collaboration

Benefits

  • Medical, dental, and vision insurance
  • 401K with employer match
  • Commuter benefits
  • Catered lunch multiple days weekly
  • Dinner stipend for late-night work
  • Grubhub subscription and 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.

View all jobs at Mirage

Likely interview questions

  • Describe a complex backend system you've built end-to-end—what were the scaling challenges and how did you solve them?
  • What experience do you have integrating or deploying large generative AI models in production, and what were the key learnings?
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