Mirage
Software Engineer, Agents
About this role
Mirage seeks a Software Engineer to design and build agentic systems that power creative workflows for AI-driven video editing. You'll work end-to-end on agent architectures, integrate cutting-edge models, and deploy production systems that help users create videos through natural language.
What you'll do
- Build and ship end-to-end agentic systems that improve creative workflows with focus on quality, performance, and reliability
- Design agent architectures including context gathering, planning, tool selection, and task execution at scale
- Integrate state-of-the-art models combining internal research and external capabilities for new agent experiences
- Measure and improve agent quality in production using experimentation, evaluation frameworks, and user feedback
- Own meaningful problems end-to-end across product, machine learning, and systems engineering
- Define how agents are built and deployed across the product
What they're looking for
- ML systems and agentic pipelines in production
- Context engineering (RAG, token optimization, context management)
- Evaluation systems and agentic infrastructure design
- LLM fine-tuning for specific use cases
- Multi-agent architectures
- Problem solving and rapid learning
- End-to-end project scoping and delivery
- Generative media and agentic AI technology
Benefits
- Comprehensive medical, dental, and vision coverage
- 401K with employer match
- Commuter benefits
- Catered lunch multiple days per week
- Dinner stipend for evening work
- In-person collaborative environment in Union Square, NYC
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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 MirageLikely interview questions
- Describe a production ML system or agentic pipeline you built—what were the key architectural decisions and how did you measure success?
- How have you approached context engineering in RAG or retrieval systems, and what optimizations did you implement for scale?