Research Engineer, Agentic Systems

Mirage is an AI-native video platform that intelligently orchestrates production and editing through natural language. Our models leverage contextual awareness to execute the same creative decisions a professional editor would — dramatically improving productivity for experienced teams, while making video creation accessible to anyone.

We’re an interdisciplinary team addressing some of the most difficult technical and creative challenges in generative media. As an early member of our team, you’ll tackle foundational problems that remain largely unsolved across the industry, driving an outsized impact on the future of creative expression.

Product (Captions by Mirage)

Research (Our Models and Agents)

Updates (Mirage on X / twitter)

TechCrunch, Forbes AI 50, Fast Company (press)

We’re very fortunate to have some the best investors and entrepreneurs backing us, including Index Ventures, Kleiner Perkins, Sequoia Capital, Andreessen Horowitz, General Catalyst, Uncommon Projects, Kevin Systrom, Mike Krieger, Lenny Rachitsky, Antoine Martin, Julie Zhuo, Ben Rubin, Jaren Glover, SVAngel, 20VC, Ludlow Ventures, Chapter One, and more.

Please note that all of our roles will require you to be in-person at our NYC HQ (located in Union Square)

• Design and build end-to-end agentic systems for creative tasks

• Develop novel approaches for training and adapting the large language models that power these agents

• Design new 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 in real-world tasks

• Design evaluation frameworks for agentic workflows in video analysis and editing

• Analyze failure modes across the full agent loop (planning, tool use, execution) and iterate on improvements

• BS/MS/PhD in CS, ML, or related field

• Strong track record building production ML systems or agentic pipelines

• Deep understanding of transformers and modern LLM techniques

• Experience with fine-tuning, alignment, or post-training methods, especially for adapting models to generate structured outputs or drive tool use

• Comfort owning the full stack, from model-level experiments to deployed agent systems

• Strong experimental rigor and good taste for what makes agents actually work in practice