ML Engineer, Generative Video

About Mirage

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.

Resources:

• Product (Captions by Mirage)
• Research (Seeing Voices, technical-white-paper)
• Updates (Mirage on X / twitter)
• Press: TechCrunch, Forbes AI 50, Fast Company

Investors: 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.

Location: All roles require in-person work 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

Benefits

• 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

Please note: Benefits apply to full time employees only.

Equal Opportunity

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.