ML Engineer, Generative Video

Mirage

New York, NY FULL_TIME USD175,000.00 - 275,000.00 per year 2026-06-30

About Mirage

Mirage is an AI-native video platform that intelligently orchestrates production and editing through natural language. They have been featured in TechCrunch, Forbes AI 50, and Fast Company. Their investors include 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, and Chapter One.

About the Position

Introduction

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.

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

Requirements

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