About the role
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. More
about us Product (Captions by Mirage) Research (Seeing Voices, technical-white-paper) Updates (Mirage on X / twitter) TechCrunch , Forbes AI 50 , Fast Company (press) Our Investors 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)
About
the Role Mirage is seeking a Research Engineer to build and scale systems for training and deploying large language models for multimodal creative tasks, specifically focusing on video analysis pipelines. You’ll work closely with researchers to turn ideas into high-performance systems that power new product capabilities. This role emphasizes efficiency, scalability, and robustness across the full LLM lifecycle.
Responsibilities Build pipelines for large-scale LLM training and fine-tuning, optimizing data loaders for high-bandwidth multimodal datasets Optimize training and inference performance Implement distributed systems for scalable experimentation Improve latency, throughput, and cost efficiency for complex multimodal inputs and structured generation Support deployment of LLM systems into production environments Develop tools for evaluation, monitoring, and iteration What makes you a great fit 2+ years of professional industry experience Strong experience with large-scale ML systems Proficiency in PyTorch, CUDA, Triton, and distributed training Experience optimizing LLM training or inference Strong systems intuition (memory, compute, throughput tradeoffs) Ability to bridge research and production Strong software engineering and debugging skills