Mirage
Software Engineer, Agents
About this role
Mirage is seeking a Software Engineer to design and build agentic systems for AI-powered video editing. You'll work across product and ML to develop agents that automate creative workflows, integrating state-of-the-art models and building scalable infrastructure for short-form video production.
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 execution at scale
- Integrate state-of-the-art models combining internal research and external capabilities
- Measure and improve agent quality in production using experimentation and evaluation frameworks
- Own meaningful problems end-to-end across product, ML, and systems engineering
- Define how agents are built and deployed across the product
What they're looking for
- ML systems and agentic pipeline development in production
- Context engineering and RAG systems
- Token optimization and context management at scale
- Evaluation systems and agentic infrastructure design
- Multi-agent architectures
- LLM fine-tuning
- Fast-paced problem solving and end-to-end project delivery
- Python or systems programming languages
Benefits
- Comprehensive medical, dental, and vision plans
- 401K with employer match
- Commuter benefits
- Catered lunch multiple days per week
- Dinner stipend when working late
- Early-stage startup equity upside
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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
- Walk us through a production ML system or agentic pipeline you built—what were the key technical challenges and how did you measure success?
- How have you approached context engineering and RAG systems? What optimization techniques have you used to manage token efficiency at scale?