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
Comprehensive medical, dental, and vision plans
401K with employer match
Commuter Benefits
Catered lunch multiple days per week
Dinner stipend every night if you're working late and want a bite!
Grubhub subscription
Health & Wellness Perks
Multiple team offsites per year with team events every month
Generous PTO policy
Captions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Please note benefits apply to full time employees only.
Based on 638 disclosed AI Engineering salaries on RoleSuite, the role pays a median of $203K/year, with most offers between $162K and $246K (10th–90th percentile: $131K–$285K).
This posting lists $175K–$275K, above the $203K market median.
See the full AI Engineering salary breakdown →