Mirage
ML Engineer, Agentic Systems
About this role
Mirage seeks an ML Engineer to develop agentic systems that leverage large language models for multimodal creative video tasks. You'll design end-to-end agents, advance reasoning and control mechanisms, and build novel fine-tuning strategies to enable intelligent video editing and analysis at scale.
What you'll do
- Design and build end-to-end agentic systems for creative video tasks
- Develop novel training and adaptation approaches for LLMs powering agents
- Create objectives, datasets, and fine-tuning strategies to improve agent behavior and reliability
- Explore multimodal reasoning and structured generation for creative control
- Run systematic experiments to evaluate and improve agent performance on real-world tasks
- Design evaluation frameworks and analyze failure modes across the full agent loop
What they're looking for
- Large language models and transformer architectures
- Agentic systems and tool use design
- Fine-tuning, alignment, and post-training methods
- Multimodal reasoning and structured output generation
- Production ML systems development
- Experimental rigor and systematic evaluation
- Video understanding and processing
- Full-stack ownership from research to deployment
Benefits
- Comprehensive medical, dental, and vision coverage
- 401K with employer match
- Commuter benefits and meal perks (catered lunches, dinner stipend, Grubhub subscription)
- Health and wellness programs
- Multiple team offsites per year with monthly events
- Generous PTO policy
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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 agentic system you've built—how did you handle failures in planning, tool use, or execution?
- Walk us through your approach to designing fine-tuning strategies for an LLM to generate structured outputs or control tool use.