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Mirage

Research Engineer, Agentic Systems

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

About this role

Mirage is hiring a Research Engineer to develop agentic systems that use large language models for video editing and creative tasks. You'll design end-to-end agent architectures, advance LLM capabilities for multimodal reasoning, and evaluate agent performance on real-world video workflows.

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
  • Explore multimodal reasoning and structured generation for creative control
  • Run systematic experiments to evaluate and improve agent performance
  • Design evaluation frameworks and analyze failure modes across agent loops

What they're looking for

  • Large language models and transformer architectures
  • Fine-tuning, alignment, and post-training methods
  • Agentic systems and tool use design
  • Multimodal reasoning and structured generation
  • Production ML systems and full-stack ownership
  • Experimental design and evaluation frameworks
  • Video analysis and understanding
  • Python and ML frameworks

Benefits

  • Medical, dental, and vision insurance
  • 401(k) with employer match
  • Catered lunch multiple days per week
  • Dinner stipend for late nights and Grubhub subscription
  • Multiple team offsites and monthly team 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 Mirage

Likely interview questions

  • Describe a complex agentic system you've built—what were the key challenges in getting agents to reliably execute multi-step plans?
  • Walk us through your approach to designing evaluation metrics for agent behavior. How do you measure success beyond task completion?