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 an ML Engineer to build and scale the systems powering our video generation models. You’ll work on novel modeling approaches, training objectives, scaling strategies, and inference optimization and efficiency to bring cutting-edge models into production. This role sits at the intersection of research and systems engineering, focusing on making advanced models faster, more efficient, and capable of ultra-low latency, real-time generation.
Responsibilities Train and optimize large-scale video and multimodal models Improve efficiency across training and inference (memory, latency, cost) Implement techniques such as distillation, quantization, and pruning to aggressively accelerate diffusion and autoregressive generation Build and maintain distributed training systems Optimize GPU utilization, parallelism, and throughput Develop tooling for experimentation, evaluation, and debugging Translate research models into robust, production-ready systems Monitor and improve model performance in real-world usage What makes you a great fit BS/MS/PhD in CS, ML, or related field 2+ years of professional industry experience Strong experience in deep learning systems and infrastructure Expertise in PyTorch, CUDA, Triton, and distributed training (FSDP, etc.) Experience scaling and optimizing large models under low-latency inference constraints Strong debugging and performance profiling skills Ability to move quickly from prototype to production