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Mirage

Software Engineer, Backend

Union Square, New York City$175k–$300kfulltimemidAdded 3 days ago

About this role

Mirage seeks a Backend Software Engineer to design and operate production systems powering an AI-native video platform. You'll build scalable services, APIs, and ML pipelines while integrating generative models into product experiences, working closely with cross-functional teams in a fast-paced startup environment.

What you'll do

  • Design, build, and own backend systems end-to-end including services, APIs, data pipelines, and infrastructure
  • Solve complex technical challenges in distributed systems, scaling, concurrency, and performance optimization
  • Deploy, serve, and scale generative AI models in production environments
  • Instrument and experiment in production to improve system and product quality
  • Build and operate core platform infrastructure including third-party integrations and internal APIs
  • Partner 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
  • APIs and service-oriented architecture
  • Data pipelines and infrastructure
  • Production machine learning operations
  • Performance optimization and debugging
  • Problem-solving and rapid iteration
  • Cross-functional collaboration

Benefits

  • Medical, dental, and vision insurance
  • 401K with employer match
  • Commuter benefits
  • Catered lunches multiple days weekly
  • Dinner stipends 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.

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Likely interview questions

  • Walk us through a backend system you designed end-to-end—what were the scaling challenges and how did you solve them?
  • Describe your experience deploying and operating machine learning models in production. What complications did you encounter?
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